Database Server Embedded Process and Code Accelerator

ABSTRACT

A computing system receives a program. The program is in a first computer language and specifies computer operations on stored data. The computing system is configured to partition the stored data into sets of partitioned data for performing parallel execution on each of the sets of partitioned data. The computing system determines whether the program comprises a thread program component. The computing system, responsive to determining that the program comprises a thread program component, generates computer-generated computer instructions. The computer-generated computer instructions are in a second computer language. The computer-generated computer instructions are dependent on whether the thread program component specifies information for partitioning and grouping the stored data; whether the program comprises a data program component; or whether the data program component specifies information for partitioning and grouping the output data of the thread program component. The computing system executes the program according to the computer-generated computer instructions.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/832,848, filed Apr. 11, 2019, the disclosure of which is incorporatedherein by reference in their entirety.

SUMMARY

In an example embodiment, a computer-program product tangibly embodiedin a non-transitory machine-readable storage medium is provided. Thecomputer-program product includes instructions to cause a computingsystem to receive, at a server of the computing system, a program. Theprogram is in a first computer language and specifies computeroperations on stored data. The computing system is configured topartition the stored data into multiple sets of partitioned data forperforming parallel execution of one or more of the computer operationson each of the multiple sets of partitioned data. The computer-programproduct includes instructions to cause a computing system to determinewhether the program comprises a thread program component. Threadoperations of the thread program component comprise computerinstructions for execution in parallel of the one or more of thecomputer operations on each of the multiple sets of partitioned data.The computer-program product includes instructions to cause a computingsystem, responsive to determining that the program comprises a threadprogram component, to generate, at the server, computer-generatedcomputer instructions. The computer-generated computer instructions arein a second computer language different than the first computer languageand are for executing the one or more of the computer operations inparallel. The computer-generated computer instructions are dependent onone or more of: whether the thread program component specifies data keyinformation for partitioning and grouping the stored data using a firstkey indicated by the data key information; whether the program comprisesa data program component comprising data program instructions foroperations capable of execution in parallel on output data that isoutput from execution of the thread program component; and whether thedata program component specifies output key information for partitioningand grouping the output data of the thread program component using asecond key indicated by the output key information. The computer-programproduct includes instructions to cause a computing system to execute, bythe server, the program according to the computer-generated computerinstructions.

In another example embodiment, a computing system is provided. Thecomputing system includes, but is not limited to, a processor andmemory. The memory contains instructions that when executed by theprocessor control the computing system to receive a program, and toexecute the program according to computer-generated computerinstructions.

In another example embodiment, a method of receiving a program andexecuting the program according to computer-generated computerinstructions is provided.

Other features and aspects of example embodiments are presented below inthe Detailed Description when read in connection with the drawingspresented with this application.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram that provides an illustration of thehardware components of a computing system, according to at least oneembodiment of the present technology.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to at least one embodiment of the present technology.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to at least one embodiment ofthe present technology.

FIG. 4 illustrates a communications grid computing system including avariety of control and worker nodes, according to at least oneembodiment of the present technology.

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to at least one embodiment of the presenttechnology.

FIG. 6 illustrates a portion of a communications grid computing systemincluding a control node and a worker node, according to at least oneembodiment of the present technology.

FIG. 7 illustrates a flow chart showing an example process for executinga data analysis or processing project, according to at least oneembodiment of the present technology.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to at least one embodiment ofthe present technology.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according toat least one embodiment of the present technology.

FIG. 10 illustrates an ESP system interfacing between a publishingdevice and multiple event subscribing devices, according to at least oneembodiment of the present technology.

FIG. 11 illustrates a flow chart of an example of a process forgenerating and using a machine-learning model according to at least oneembodiment of the present technology.

FIG. 12 illustrates an example of a machine-learning model as a neuralnetwork.

FIG. 13 illustrates a block diagram of a system in at least oneembodiment of the present technology.

FIG. 14 illustrates a flow diagram for executing a program in at leastone embodiment of the present technology.

FIG. 15 illustrates a block diagram of a computing system in at leastone embodiment of the present technology.

FIG. 16 illustrates a block diagram of a driver container in at leastone embodiment of the present technology.

FIG. 17A illustrates a block diagram of an executor container forpartitioned data in at least one embodiment of the present technology.

FIG. 17B illustrates a flow diagram for an executor container in atleast one embodiment of the present technology.

FIG. 18 illustrates an example program and program indicators in atleast one embodiment of the present technology.

FIG. 19A illustrates a flow diagram for a data table according toprogram indicators in at least one embodiment of the present technology.

FIG. 19B illustrates a flow diagram for a data file according to programindicators in at least one embodiment of the present technology.

FIG. 19C illustrates a received program in at least one embodiment ofthe present technology.

FIG. 20A illustrates a flow diagram for a data table according toprogram indicators in at least one embodiment of the present technology.

FIG. 20B illustrates a flow diagram for a data file according to programindicators in at least one embodiment of the present technology.

FIG. 20C illustrates a received program in at least one embodiment ofthe present technology.

FIG. 21A illustrates a flow diagram for a data table according toprogram indicators in at least one embodiment of the present technology.

FIG. 21B illustrates a flow diagram for a data file according to programindicators in at least one embodiment of the present technology.

FIG. 21C illustrates a received program in at least one embodiment ofthe present technology.

FIG. 22A illustrates a flow diagram for a data table according toprogram indicators in at least one embodiment of the present technology.

FIG. 22B illustrates a flow diagram for a data file according to programindicators in at least one embodiment of the present technology.

FIG. 22C illustrates a received program in at least one embodiment ofthe present technology.

FIG. 23A illustrates a flow diagram for a data table according toprogram indicators in at least one embodiment of the present technology.

FIG. 23B illustrates a flow diagram for a data file according to programindicators in at least one embodiment of the present technology.

FIG. 23C illustrates a received program in at least one embodiment ofthe present technology.

FIG. 24A illustrates a flow diagram for a data table according toprogram indicators in at least one embodiment of the present technology.

FIG. 24B illustrates a flow diagram for a data file according to programindicators in at least one embodiment of the present technology.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofembodiments of the technology. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive.

The ensuing description provides example embodiments only, and is notintended to limit the scope, applicability, or configuration of thedisclosure. Rather, the ensuing description of the example embodimentswill provide those skilled in the art with an enabling description forimplementing an example embodiment. It should be understood that variouschanges may be made in the function and arrangement of elements withoutdeparting from the spirit and scope of the technology as set forth inthe appended claims.

Specific details are given in the following description to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits,systems, networks, processes, and other components may be shown ascomponents in block diagram form in order not to obscure the embodimentsin unnecessary detail. In other instances, well-known circuits,processes, algorithms, structures, and techniques may be shown withoutunnecessary detail in order to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchartmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process is terminatedwhen its operations are completed, but could have additional operationsnot included in a figure. A process may correspond to a method, afunction, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination can correspond to a return ofthe function to the calling function or the main function.

Systems depicted in some of the figures may be provided in variousconfigurations. In some embodiments, the systems may be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

FIG. 1 is a block diagram that provides an illustration of the hardwarecomponents of a data transmission network 100, according to embodimentsof the present technology. Data transmission network 100 is aspecialized computer system that may be used for processing largeamounts of data where a large number of computer processing cycles arerequired.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. Data transmission network 100 also includes one or morenetwork devices 102. Network devices 102 may include client devices thatattempt to communicate with computing environment 114. For example,network devices 102 may send data to the computing environment 114 to beprocessed, may send signals to the computing environment 114 to controldifferent aspects of the computing environment or the data it isprocessing, among other reasons. Network devices 102 may interact withthe computing environment 114 through a number of ways, such as, forexample, over one or more networks 108. As shown in FIG. 1, computingenvironment 114 may include one or more other systems. For example,computing environment 114 may include a database system 118 and/or acommunications grid 120.

In other embodiments, network devices may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP), described further with respect to FIGS.8-10), to the computing environment 114 via networks 108. For example,network devices 102 may include network computers, sensors, databases,or other devices that may transmit or otherwise provide data tocomputing environment 114. For example, network devices may includelocal area network devices, such as routers, hubs, switches, or othercomputer networking devices. These devices may provide a variety ofstored or generated data, such as network data or data specific to thenetwork devices themselves. Network devices may also include sensorsthat monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devicesmay provide data they collect over time. Network devices may alsoinclude devices within the internet of things, such as devices within ahome automation network. Some of these devices may be referred to asedge devices, and may involve edge computing circuitry. Data may betransmitted by network devices directly to computing environment 114 orto network-attached data stores, such as network-attached data stores110 for storage so that the data may be retrieved later by the computingenvironment 114 or other portions of data transmission network 100.

Data transmission network 100 may also include one or morenetwork-attached data stores 110. Network-attached data stores 110 areused to store data to be processed by the computing environment 114 aswell as any intermediate or final data generated by the computing systemin non-volatile memory. However in certain embodiments, theconfiguration of the computing environment 114 allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory (e.g., disk). This can be useful in certain situations, such aswhen the computing environment 114 receives ad hoc queries from a userand when responses, which are generated by processing large amounts ofdata, need to be generated on-the-fly. In this non-limiting situation,the computing environment 114 may be configured to retain the processedinformation within memory so that responses can be generated for theuser at different levels of detail as well as allow a user tointeractively query against this information.

Network-attached data stores may store a variety of different types ofdata organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data storage mayinclude storage other than primary storage located within computingenvironment 114 that is directly accessible by processors locatedtherein. Network-attached data storage may include secondary, tertiaryor auxiliary storage, such as large hard drives, servers, virtualmemory, among other types. Storage devices may include portable ornon-portable storage devices, optical storage devices, and various othermediums capable of storing, containing data. A machine-readable storagemedium or computer-readable storage medium may include a non-transitorymedium in which data can be stored and that does not include carrierwaves and/or transitory electronic signals. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode and/or machine-executable instructions that may represent aprocedure, a function, a subprogram, a program, a routine, a subroutine,a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, amongothers. Furthermore, the data stores may hold a variety of differenttypes of data. For example, network-attached data stores 110 may holdunstructured (e.g., raw) data, such as manufacturing data (e.g., adatabase containing records identifying products being manufactured withparameter data for each product, such as colors and models) or productsales databases (e.g., a database containing individual data recordsidentifying details of individual product sales).

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data and/or structured hierarchically according toone or more dimensions (e.g., parameters, attributes, and/or variables).For example, data may be stored in a hierarchical data structure, suchas a ROLAP OR MOLAP database, or may be stored in another tabular form,such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the one or more sever farms 106 or one or more servers within theserver farms. Server farms 106 can be configured to provide informationin a predetermined manner. For example, server farms 106 may access datato transmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, and/or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or moresensors, as inputs from a control database, or may have been received asinputs from an external system or device. Server farms 106 may assist inprocessing the data by turning raw data into processed data based on oneor more rules implemented by the server farms. For example, sensor datamay be analyzed to determine changes in an environment over time or inreal-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain embodiments, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1. Services provided by the cloudnetwork can dynamically scale to meet the needs of its users. The cloudnetwork 116 may include one or more computers, servers, and/or systems.In some embodiments, the computers, servers, and/or systems that make upthe cloud network 116 are different from the user's own on-premisescomputers, servers, and/or systems. For example, the cloud network 116may host an application, and a user may, via a communication networksuch as the Internet, on demand, order and use the application.

While each device, server and system in FIG. 1 is shown as a singledevice, it will be appreciated that multiple devices may instead beused. For example, a set of network devices can be used to transmitvarious communications from a single user, or remote server 140 mayinclude a server stack. As another example, data may be processed aspart of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between a device and connection management system 150,between servers 106 and computing environment 114 or between a serverand a device) may occur over one or more networks 108. Networks 108 mayinclude one or more of a variety of different types of networks,including a wireless network, a wired network, or a combination of awired and wireless network. Examples of suitable networks include theInternet, a personal area network, a local area network (LAN), a widearea network (WAN), or a wireless local area network (WLAN). A wirelessnetwork may include a wireless interface or combination of wirelessinterfaces. As an example, a network in the one or more networks 108 mayinclude a short-range communication channel, such as a Bluetooth or aBluetooth Low Energy channel. A wired network may include a wiredinterface. The wired and/or wireless networks may be implemented usingrouters, access points, bridges, gateways, or the like, to connectdevices in the network 114, as will be further described with respect toFIG. 2. The one or more networks 108 can be incorporated entirely withinor can include an intranet, an extranet, or a combination thereof. Inone embodiment, communications between two or more systems and/ordevices can be achieved by a secure communications protocol, such assecure sockets layer (SSL) or transport layer security (TLS). Inaddition, data and/or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings and/or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics. IoT may be implemented in various areas, such asfor access (technologies that get data and move it), embed-ability(devices with embedded sensors), and services. Industries in the IoTspace may automotive (connected car), manufacturing (connected factory),smart cities, energy and retail. This will be described further belowwith respect to FIG. 2.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The compute nodes in the grid-basedcomputing system 120 and the transmission network database system 118may share the same processor hardware, such as processors that arelocated within computing environment 114.

FIG. 2 illustrates an example network including an example set ofdevices communicating with each other over an exchange system and via anetwork, according to embodiments of the present technology. As noted,each communication within data transmission network 100 may occur overone or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. For example,network device 204 may collect data either from its surroundingenvironment or from other network devices (such as network devices205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, electrical current, among others.The sensors may be mounted to various components used as part of avariety of different types of systems (e.g., an oil drilling operation).The network devices may detect and record data related to theenvironment that it monitors, and transmit that data to computingenvironment 214.

As noted, one type of system that may include various sensors thatcollect data to be processed and/or transmitted to a computingenvironment according to certain embodiments includes an oil drillingsystem. For example, the one or more drilling operation sensors mayinclude surface sensors that measure a hook load, a fluid rate, atemperature and a density in and out of the wellbore, a standpipepressure, a surface torque, a rotation speed of a drill pipe, a rate ofpenetration, a mechanical specific energy, etc. and downhole sensorsthat measure a rotation speed of a bit, fluid densities, downholetorque, downhole vibration (axial, tangential, lateral), a weightapplied at a drill bit, an annular pressure, a differential pressure, anazimuth, an inclination, a dog leg severity, a measured depth, avertical depth, a downhole temperature, etc. Besides the raw datacollected directly by the sensors, other data may include parameterseither developed by the sensors or assigned to the system by a client orother controlling device. For example, one or more drilling operationcontrol parameters may control settings such as a mud motor speed toflow ratio, a bit diameter, a predicted formation top, seismic data,weather data, etc. Other data may be generated using physical modelssuch as an earth model, a weather model, a seismic model, a bottom holeassembly model, a well plan model, an annular friction model, etc. Inaddition to sensor and control settings, predicted outputs, of forexample, the rate of penetration, mechanical specific energy, hook load,flow in fluid rate, flow out fluid rate, pump pressure, surface torque,rotation speed of the drill pipe, annular pressure, annular frictionpressure, annular temperature, equivalent circulating density, etc. mayalso be stored in the data warehouse.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a homeautomation or similar automated network in a different environment, suchas an office space, school, public space, sports venue, or a variety ofother locations. Network devices in such an automated network mayinclude network devices that allow a user to access, control, and/orconfigure various home appliances located within the user's home (e.g.,a television, radio, light, fan, humidifier, sensor, microwave, iron,and/or the like), or outside of the user's home (e.g., exterior motionsensors, exterior lighting, garage door openers, sprinkler systems, orthe like). For example, network device 102 may include a home automationswitch that may be coupled with a home appliance. In another embodiment,a network device can allow a user to access, control, and/or configuredevices, such as office-related devices (e.g., copy machine, printer, orfax machine), audio and/or video related devices (e.g., a receiver, aspeaker, a projector, a DVD player, or a television), media-playbackdevices (e.g., a compact disc player, a CD player, or the like),computing devices (e.g., a home computer, a laptop computer, a tablet, apersonal digital assistant (PDA), a computing device, or a wearabledevice), lighting devices (e.g., a lamp or recessed lighting), devicesassociated with a security system, devices associated with an alarmsystem, devices that can be operated in an automobile (e.g., radiodevices, navigation devices), and/or the like. Data may be collectedfrom such various sensors in raw form, or data may be processed by thesensors to create parameters or other data either developed by thesensors based on the raw data or assigned to the system by a client orother controlling device.

In another example, another type of system that may include varioussensors that collect data to be processed and/or transmitted to acomputing environment according to certain embodiments includes a poweror energy grid. A variety of different network devices may be includedin an energy grid, such as various devices within one or more powerplants, energy farms (e.g., wind farm, solar farm, among others) energystorage facilities, factories, homes and businesses of consumers, amongothers. One or more of such devices may include one or more sensors thatdetect energy gain or loss, electrical input or output or loss, and avariety of other efficiencies. These sensors may collect data to informusers of how the energy grid, and individual devices within the grid,may be functioning and how they may be made more efficient.

Network device sensors may also perform processing on data it collectsbefore transmitting the data to the computing environment 114, or beforedeciding whether to transmit data to the computing environment 114. Forexample, network devices may determine whether data collected meetscertain rules, for example by comparing data or values calculated fromthe data and comparing that data to one or more thresholds. The networkdevice may use this data and/or comparisons to determine if the datashould be transmitted to the computing environment 214 for further useor processing.

Computing environment 214 may include machines 220 and 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines, 220and 240, computing environment 214 may have only one machine or may havemore than two machines. The machines that make up computing environment214 may include specialized computers, servers, or other machines thatare configured to individually and/or collectively process large amountsof data. The computing environment 214 may also include storage devicesthat include one or more databases of structured data, such as dataorganized in one or more hierarchies, or unstructured data. Thedatabases may communicate with the processing devices within computingenvironment 214 to distribute data to them. Since network devices maytransmit data to computing environment 214, that data may be received bythe computing environment 214 and subsequently stored within thosestorage devices. Data used by computing environment 214 may also bestored in data stores 235, which may also be a part of or connected tocomputing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withdevices 230 via one or more routers 225. Computing environment 214 maycollect, analyze and/or store data from or pertaining to communications,client device operations, client rules, and/or user-associated actionsstored at one or more data stores 235. Such data may influencecommunication routing to the devices within computing environment 214,how data is stored or processed within computing environment 214, amongother actions.

Notably, various other devices can further be used to influencecommunication routing and/or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include aweb server 240. Thus, computing environment 214 can retrieve data ofinterest, such as client information (e.g., product information, clientrules, etc.), technical product details, news, current or predictedweather, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices may receive data periodically from network device sensors as thesensors continuously sense, monitor and track changes in theirenvironments. Devices within computing environment 214 may also performpre-analysis on data it receives to determine if the data receivedshould be processed as part of an ongoing project. The data received andcollected by computing environment 214, no matter what the source ormethod or timing of receipt, may be processed over a period of time fora client to determine results data based on the client's needs andrules.

FIG. 3 illustrates a representation of a conceptual model of acommunications protocol system, according to embodiments of the presenttechnology. More specifically, FIG. 3 identifies operation of acomputing environment in an Open Systems Interaction model thatcorresponds to various connection components. The model 300 shows, forexample, how a computing environment, such as computing environment 314(or computing environment 214 in FIG. 2) may communicate with otherdevices in its network, and control how communications between thecomputing environment and other devices are executed and under whatconditions.

The model can include layers 302-314. The layers are arranged in astack. Each layer in the stack serves the layer one level higher than it(except for the application layer, which is the highest layer), and isserved by the layer one level below it (except for the physical layer,which is the lowest layer). The physical layer is the lowest layerbecause it receives and transmits raw bites of data, and is the farthestlayer from the user in a communications system. On the other hand, theapplication layer is the highest layer because it interacts directlywith a software application.

As noted, the model includes a physical layer 302. Physical layer 302represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagnetic signals.Physical layer 302 also defines protocols that may controlcommunications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (i.e.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 defines the protocol for routing within a network. Inother words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission and/or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt and/or format data based on data types known to beaccepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availabilityand/or communication content or formatting using the applications.

Intra-network connection components 322 and 324 are shown to operate inlower levels, such as physical layer 302 and link layer 304,respectively. For example, a hub can operate in the physical layer, aswitch can operate in the physical layer, and a router can operate inthe network layer. Inter-network connection components 326 and 328 areshown to operate on higher levels, such as layers 306-314. For example,routers can operate in the network layer and network devices can operatein the transport, session, presentation, and application layers.

As noted, a computing environment 314 can interact with and/or operateon, in various embodiments, one, more, all or any of the various layers.For example, computing environment 314 can interact with a hub (e.g.,via the link layer) so as to adjust which devices the hub communicateswith. The physical layer may be served by the link layer, so it mayimplement such data from the link layer. For example, the computingenvironment 314 may control which devices it will receive data from. Forexample, if the computing environment 314 knows that a certain networkdevice has turned off, broken, or otherwise become unavailable orunreliable, the computing environment 314 may instruct the hub toprevent any data from being transmitted to the computing environment 314from that network device. Such a process may be beneficial to avoidreceiving data that is inaccurate or that has been influenced by anuncontrolled environment. As another example, computing environment 314can communicate with a bridge, switch, router or gateway and influencewhich device within the system (e.g., system 200) the component selectsas a destination. In some embodiments, computing environment 314 caninteract with various layers by exchanging communications with equipmentoperating on a particular layer by routing or modifying existingcommunications. In another embodiment, such as in a grid computingenvironment, a node may determine how data within the environment shouldbe routed (e.g., which node should receive certain data) based oncertain parameters or information provided by other layers within themodel.

As noted, the computing environment 314 may be a part of acommunications grid environment, the communications of which may beimplemented as shown in the protocol of FIG. 3. For example, referringback to FIG. 2, one or more of machines 220 and 240 may be part of acommunications grid computing environment. A gridded computingenvironment may be employed in a distributed system with non-interactiveworkloads where data resides in memory on the machines, or computenodes. In such an environment, analytic code, instead of a databasemanagement system, controls the processing performed by the nodes. Datais co-located by pre-distributing it to the grid nodes, and the analyticcode on each node loads the local data into memory. Each node may beassigned a particular task such as a portion of a processing project, orto organize or control other nodes within the grid.

FIG. 4 illustrates a communications grid computing system 400 includinga variety of control and worker nodes, according to embodiments of thepresent technology. Communications grid computing system 400 includesthree control nodes and one or more worker nodes. Communications gridcomputing system 400 includes control nodes 402, 404, and 406. Thecontrol nodes are communicatively connected via communication paths 451,453, and 455. Therefore, the control nodes may transmit information(e.g., related to the communications grid or notifications), to andreceive information from each other. Although communications gridcomputing system 400 is shown in FIG. 4 as including three controlnodes, the communications grid may include more or less than threecontrol nodes.

Communications grid computing system (or just “communications grid”) 400also includes one or more worker nodes. Shown in FIG. 4 are six workernodes 410-420. Although FIG. 4 shows six worker nodes, a communicationsgrid according to embodiments of the present technology may include moreor less than six worker nodes. The number of worker nodes included in acommunications grid may be dependent upon how large the project or dataset is being processed by the communications grid, the capacity of eachworker node, the time designated for the communications grid to completethe project, among others. Each worker node within the communicationsgrid 400 may be connected (wired or wirelessly, and directly orindirectly) to control nodes 402-406. Therefore, each worker node mayreceive information from the control nodes (e.g., an instruction toperform work on a project) and may transmit information to the controlnodes (e.g., a result from work performed on a project). Furthermore,worker nodes may communicate with each other (either directly orindirectly). For example, worker nodes may transmit data between eachother related to a job being performed or an individual task within ajob being performed by that worker node. However, in certainembodiments, worker nodes may not, for example, be connected(communicatively or otherwise) to certain other worker nodes. In anembodiment, worker nodes may only be able to communicate with thecontrol node that controls it, and may not be able to communicate withother worker nodes in the communications grid, whether they are otherworker nodes controlled by the control node that controls the workernode, or worker nodes that are controlled by other control nodes in thecommunications grid.

A control node may connect with an external device with which thecontrol node may communicate (e.g., a grid user, such as a server orcomputer, may connect to a controller of the grid). For example, aserver or computer may connect to control nodes and may transmit aproject or job to the node. The project may include a data set. The dataset may be of any size. Once the control node receives such a projectincluding a large data set, the control node may distribute the data setor projects related to the data set to be performed by worker nodes.Alternatively, for a project including a large data set, the data setmay be receive or stored by a machine other than a control node (e.g., aHadoop data node).

Control nodes may maintain knowledge of the status of the nodes in thegrid (i.e., grid status information), accept work requests from clients,subdivide the work across worker nodes, coordinate the worker nodes,among other responsibilities. Worker nodes may accept work requests froma control node and provide the control node with results of the workperformed by the worker node. A grid may be started from a single node(e.g., a machine, computer, server, etc.). This first node may beassigned or may start as the primary control node that will control anyadditional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (i.e., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node receives aproject, the primary control node may distribute portions of the projectto its worker nodes for execution. For example, when a project isinitiated on communications grid 400, primary control node 402 controlsthe work to be performed for the project in order to complete theproject as requested or instructed. The primary control node maydistribute work to the worker nodes based on various factors, such aswhich subsets or portions of projects may be completed most efficientlyand in the correct amount of time. For example, a worker node mayperform analysis on a portion of data that is already local (e.g.,stored on) the worker node. The primary control node also coordinatesand processes the results of the work performed by each worker nodeafter each worker node executes and completes its job. For example, theprimary control node may receive a result from one or more worker nodes,and the control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404 and 406, may beassigned as backup control nodes for the project. In an embodiment,backup control nodes may not control any portion of the project.Instead, backup control nodes may serve as a backup for the primarycontrol node and take over as primary control node if the primarycontrol node were to fail. If a communications grid were to include onlya single control node, and the control node were to fail (e.g., thecontrol node is shut off or breaks) then the communications grid as awhole may fail and any project or job being run on the communicationsgrid may fail and may not complete. While the project may be run again,such a failure may cause a delay (severe delay in some cases, such asovernight delay) in completion of the project. Therefore, a grid withmultiple control nodes, including a backup control node, may bebeneficial.

To add another node or machine to the grid, the primary control node mayopen a pair of listening sockets, for example. A socket may be used toaccept work requests from clients, and the second socket may be used toaccept connections from other grid nodes). The primary control node maybe provided with a list of other nodes (e.g., other machines, computers,servers) that will participate in the grid, and the role that each nodewill fill in the grid. Upon startup of the primary control node (e.g.,the first node on the grid), the primary control node may use a networkprotocol to start the server process on every other node in the grid.Command line parameters, for example, may inform each node of one ormore pieces of information, such as: the role that the node will have inthe grid, the host name of the primary control node, the port number onwhich the primary control node is accepting connections from peer nodes,among others. The information may also be provided in a configurationfile, transmitted over a secure shell tunnel, recovered from aconfiguration server, among others. While the other machines in the gridmay not initially know about the configuration of the grid, thatinformation may also be sent to each other node by the primary controlnode. Updates of the grid information may also be subsequently sent tothose nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it will check to see if italready has a connection to that other node. If it does not have aconnection to that node, it may then establish a connection to thatcontrol node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. However, a hierarchy of nodes may also bedetermined using methods other than using the unique identifiers of thenodes. For example, the hierarchy may be predetermined, or may beassigned based on other predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404 and 406 (and, for example, toother control or worker nodes within the communications grid). Suchcommunications may sent periodically, at fixed time intervals, betweenknown fixed stages of the project's execution, among other protocols.The communications transmitted by primary control node 402 may be ofvaried types and may include a variety of types of information. Forexample, primary control node 402 may transmit snapshots (e.g., statusinformation) of the communications grid so that backup control node 404always has a recent snapshot of the communications grid. The snapshot orgrid status may include, for example, the structure of the grid(including, for example, the worker nodes in the grid, uniqueidentifiers of the nodes, or their relationships with the primarycontrol node) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes in thecommunications grid. The backup control nodes may receive and store thebackup data received from the primary control node. The backup controlnodes may transmit a request for such a snapshot (or other information)from the primary control node, or the primary control node may send suchinformation periodically to the backup control nodes.

As noted, the backup data may allow the backup control node to take overas primary control node if the primary control node fails withoutrequiring the grid to start the project over from scratch. If theprimary control node fails, the backup control node that will take overas primary control node may retrieve the most recent version of thesnapshot received from the primary control node and use the snapshot tocontinue the project from the stage of the project indicated by thebackup data. This may prevent failure of the project as a whole.

A backup control node may use various methods to determine that theprimary control node has failed. In one example of such a method, theprimary control node may transmit (e.g., periodically) a communicationto the backup control node that indicates that the primary control nodeis working and has not failed, such as a heartbeat communication. Thebackup control node may determine that the primary control node hasfailed if the backup control node has not received a heartbeatcommunication for a certain predetermined period of time. Alternatively,a backup control node may also receive a communication from the primarycontrol node itself (before it failed) or from a worker node that theprimary control node has failed, for example because the primary controlnode has failed to communicate with the worker node.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404and 406) will take over for failed primary control node 402 and becomethe new primary control node. For example, the new primary control nodemay be chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative embodiment, abackup control node may be assigned to be the new primary control nodeby another device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeembodiment, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeembodiment, the primary control node may transmit a communication toeach of the operable worker nodes still on the communications grid thateach of the worker nodes should purposefully fail also. After each ofthe worker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed.

FIG. 5 illustrates a flow chart showing an example process for adjustinga communications grid or a work project in a communications grid after afailure of a node, according to embodiments of the present technology.The process may include, for example, receiving grid status informationincluding a project status of a portion of a project being executed by anode in the communications grid, as described in operation 502. Forexample, a control node (e.g., a backup control node connected to aprimary control node and a worker node on a communications grid) mayreceive grid status information, where the grid status informationincludes a project status of the primary control node or a projectstatus of the worker node. The project status of the primary controlnode and the project status of the worker node may include a status ofone or more portions of a project being executed by the primary andworker nodes in the communications grid. The process may also includestoring the grid status information, as described in operation 504. Forexample, a control node (e.g., a backup control node) may store thereceived grid status information locally within the control node.Alternatively, the grid status information may be sent to another devicefor storage where the control node may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 illustrates a portion of a communications grid computing system600 including a control node and a worker node, according to embodimentsof the present technology. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viapath 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 include multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes a database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain embodiments, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 620 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 620 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client deice 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within a nodes 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 illustrates a flow chart showing an example method for executinga project within a grid computing system, according to embodiments ofthe present technology. As described with respect to FIG. 6, the GESC atthe control node may transmit data with a client device (e.g., clientdevice 630) to receive queries for executing a project and to respond tothose queries after large amounts of data have been processed. The querymay be transmitted to the control node, where the query may include arequest for executing a project, as described in operation 702. Thequery can contain instructions on the type of data analysis to beperformed in the project and whether the project should be executedusing the grid-based computing environment, as shown in operation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024 a-c, described further with respect to FIG. 10, may also subscribe to theESPE. The ESPE may determine or define how input data or event streamsfrom network devices or other publishers (e.g., network devices 204-209in FIG. 2) are transformed into meaningful output data to be consumed bysubscribers, such as for example client devices 230 in FIG. 2.

FIG. 8 illustrates a block diagram including components of an EventStream Processing Engine (ESPE), according to embodiments of the presenttechnology. ESPE 800 may include one or more projects 802. A project maybe described as a second-level container in an engine model managed byESPE 800 where a thread pool size for the project may be defined by auser. Each project of the one or more projects 802 may include one ormore continuous queries 804 that contain data flows, which are datatransformations of incoming event streams. The one or more continuousqueries 804 may include one or more source windows 806 and one or morederived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeembodiment, for example, there may be only one ESPE 800 for eachinstance of the ESP application, and ESPE 800 may have a unique enginename. Additionally, the one or more projects 802 may each have uniqueproject names, and each query may have a unique continuous query nameand begin with a uniquely named source window of the one or more sourcewindows 806. ESPE 800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 illustrates a flow chart showing an example process includingoperations performed by an event stream processing engine, according tosome embodiments of the present technology. As noted, the ESPE 800 (oran associated ESP application) defines how input event streams aretransformed into meaningful output event streams. More specifically, theESP application may define how input event streams from publishers(e.g., network devices providing sensed data) are transformed intomeaningful output event streams consumed by subscribers (e.g., a dataanalytics project being executed by a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. As furtherunderstood by a person of skill in the art, various operations may beperformed in parallel, for example, using a plurality of threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 illustrates an ESP system 1000 interfacing between publishingdevice 1022 and event subscribing devices 1024 a -c, according toembodiments of the present technology. ESP system 1000 may include ESPdevice or subsystem 1001, event publishing device 1022, an eventsubscribing device A 1024 a , an event subscribing device B 1024 b , andan event subscribing device C 1024 c . Input event streams are output toESP device 1001 by publishing device 1022. In alternative embodiments,the input event streams may be created by a plurality of publishingdevices. The plurality of publishing devices further may publish eventstreams to other ESP devices. The one or more continuous queriesinstantiated by ESPE 800 may analyze and process the input event streamsto form output event streams output to event subscribing device A 1024 a, event subscribing device B 1024 b , and event subscribing device C1024 c . ESP system 1000 may include a greater or a fewer number ofevent subscribing devices of event subscribing devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscribingdevice A 1024 a , event subscribing device B 1024 b , and eventsubscribing device C 1024 c , to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscribing device A 1024 a , event subscribingdevice B 1024 b , and event subscribing device C 1024 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of the eventpublishing device 1022.

ESP subsystem 800 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscribingdevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscribing device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscribing device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on event publishing device1022. The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 806, and subscribing client C 808 and to event subscriptiondevice A 1024 a , event subscription device B 1024 b , and eventsubscription device C 1024 c . Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscribing devices 1024 a -c. For example, subscribing client A 804,subscribing client B 806, and subscribing client C 808 may send thereceived event block object to event subscription device A 1024 a ,event subscription device B 1024 b , and event subscription device C1024 c , respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some embodiments, big data is processed for an analyticsproject after the data is received and stored. In other embodiments,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the current disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations such asthose in support of an ongoing manufacturing or drilling operation. Anembodiment of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, aprocessor and a computer-readable medium operably coupled to theprocessor. The processor is configured to execute an ESP engine (ESPE).The computer-readable medium has instructions stored thereon that, whenexecuted by the processor, cause the computing device to support thefailover. An event block object is received from the ESPE that includesa unique identifier. A first status of the computing device as active orstandby is determined. When the first status is active, a second statusof the computing device as newly active or not newly active isdetermined. Newly active is determined when the computing device isswitched from a standby status to an active status. When the secondstatus is newly active, a last published event block object identifierthat uniquely identifies a last published event block object isdetermined. A next event block object is selected from a non-transitorycomputer-readable medium accessible by the computing device. The nextevent block object has an event block object identifier that is greaterthan the determined last published event block object identifier. Theselected next event block object is published to an out-messagingnetwork device. When the second status of the computing device is notnewly active, the received event block object is published to theout-messaging network device. When the first status of the computingdevice is standby, the received event block object is stored in thenon-transitory computer-readable medium.

FIG. 11 is a flow chart of an example of a process for generating andusing a machine-learning model according to some aspects. Machinelearning is a branch of artificial intelligence that relates tomathematical models that can learn from, categorize, and makepredictions about data. Such mathematical models, which can be referredto as machine-learning models, can classify input data among two or moreclasses; cluster input data among two or more groups; predict a resultbased on input data; identify patterns or trends in input data; identifya distribution of input data in a space; or any combination of these.Examples of machine-learning models can include (i) neural networks;(ii) decision trees, such as classification trees and regression trees;(iii) classifiers, such as Naïve bias classifiers, logistic regressionclassifiers, ridge regression classifiers, random forest classifiers,least absolute shrinkage and selector (LASSO) classifiers, and supportvector machines; (iv) clusterers, such as k-means clusterers, mean-shiftclusterers, and spectral clusterers; (v) factorizers, such asfactorization machines, principal component analyzers and kernelprincipal component analyzers; and (vi) ensembles or other combinationsof machine-learning models. In some examples, neural networks caninclude deep neural networks, feed-forward neural networks, recurrentneural networks, convolutional neural networks, radial basis function(RBF) neural networks, echo state neural networks, long short-termmemory neural networks, bi-directional recurrent neural networks, gatedneural networks, hierarchical recurrent neural networks, stochasticneural networks, modular neural networks, spiking neural networks,dynamic neural networks, cascading neural networks, neuro-fuzzy neuralnetworks, or any combination of these.

Different machine-learning models may be used interchangeably to performa task. Examples of tasks that can be performed at least partially usingmachine-learning models include various types of scoring;bioinformatics; cheminformatics; software engineering; fraud detection;customer segmentation; generating online recommendations; adaptivewebsites; determining customer lifetime value; search engines; placingadvertisements in real time or near real time; classifying DNAsequences; affective computing; performing natural language processingand understanding; object recognition and computer vision; roboticlocomotion; playing games; optimization and metaheuristics; detectingnetwork intrusions; medical diagnosis and monitoring; or predicting whenan asset, such as a machine, will need maintenance.

Any number and combination of tools can be used to createmachine-learning models. Examples of tools for creating and managingmachine-learning models can include SAS® Enterprise Miner, SAS® RapidPredictive Modeler, and SAS® Model Manager, SAS Cloud Analytic Services(CAS) ®, SAS Viya ® of all which are by SAS Institute Inc. of Cary, N.C.

Machine-learning models can be constructed through an at least partiallyautomated (e.g., with little or no human involvement) process calledtraining. During training, input data can be iteratively supplied to amachine-learning model to enable the machine-learning model to identifypatterns related to the input data or to identify relationships betweenthe input data and output data. With training, the machine-learningmodel can be transformed from an untrained state to a trained state.Input data can be split into one or more training sets and one or morevalidation sets, and the training process may be repeated multipletimes. The splitting may follow a k-fold cross-validation rule, aleave-one-out-rule, a leave-p-out rule, or a holdout rule. An overviewof training and using a machine-learning model is described below withrespect to the flow chart of FIG. 11.

In block 1104, training data is received. In some examples, the trainingdata is received from a remote database or a local database, constructedfrom various subsets of data, or input by a user. The training data canbe used in its raw form for training a machine-learning model orpre-processed into another form, which can then be used for training themachine-learning model. For example, the raw form of the training datacan be smoothed, truncated, aggregated, clustered, or otherwisemanipulated into another form, which can then be used for training themachine-learning model.

In block 1106, a machine-learning model is trained using the trainingdata. The machine-learning model can be trained in a supervised,unsupervised, or semi-supervised manner. In supervised training, eachinput in the training data is correlated to a desired output. Thisdesired output may be a scalar, a vector, or a different type of datastructure such as text or an image. This may enable the machine-learningmodel to learn a mapping between the inputs and desired outputs. Inunsupervised training, the training data includes inputs, but notdesired outputs, so that the machine-learning model has to findstructure in the inputs on its own. In semi-supervised training, onlysome of the inputs in the training data are correlated to desiredoutputs.

In block 1108, the machine-learning model is evaluated. For example, anevaluation dataset can be obtained, for example, via user input or froma database. The evaluation dataset can include inputs correlated todesired outputs. The inputs can be provided to the machine-learningmodel and the outputs from the machine-learning model can be compared tothe desired outputs. If the outputs from the machine-learning modelclosely correspond with the desired outputs, the machine-learning modelmay have a high degree of accuracy. For example, if 90% or more of theoutputs from the machine-learning model are the same as the desiredoutputs in the evaluation dataset, the machine-learning model may have ahigh degree of accuracy. Otherwise, the machine-learning model may havea low degree of accuracy. The 90% number is an example only. A realisticand desirable accuracy percentage is dependent on the problem and thedata.

In some examples, if the machine-learning model has an inadequate degreeof accuracy for a particular task, the process can return to block 1106,where the machine-learning model can be further trained using additionaltraining data or otherwise modified to improve accuracy. If themachine-learning model has an adequate degree of accuracy for theparticular task, the process can continue to block 1110.

In block 1110, new data is received. In some examples, the new data isreceived from a remote database or a local database, constructed fromvarious subsets of data, or input by a user. The new data may be unknownto the machine-learning model. For example, the machine-learning modelmay not have previously processed or analyzed the new data.

In block 1112, the trained machine-learning model is used to analyze thenew data and provide a result. For example, the new data can be providedas input to the trained machine-learning model. The trainedmachine-learning model can analyze the new data and provide a resultthat includes a classification of the new data into a particular class,a clustering of the new data into a particular group, a prediction basedon the new data, or any combination of these.

In block 1114, the result is post-processed. For example, the result canbe added to, multiplied with, or otherwise combined with other data aspart of a job. As another example, the result can be transformed from afirst format, such as a time series format, into another format, such asa count series format. Any number and combination of operations can beperformed on the result during post-processing.

A more specific example of a machine-learning model is the neuralnetwork 1200 shown in FIG. 12. The neural network 1200 is represented asmultiple layers of interconnected neurons, such as neuron 1208, that canexchange data between one another. The layers include an input layer1202 for receiving input data, a hidden layer 1204, and an output layer1206 for providing a result. The hidden layer 1204 is referred to ashidden because it may not be directly observable or have its inputdirectly accessible during the normal functioning of the neural network1200. Although the neural network 1200 is shown as having a specificnumber of layers and neurons for exemplary purposes, the neural network1200 can have any number and combination of layers, and each layer canhave any number and combination of neurons.

The neurons and connections between the neurons can have numericweights, which can be tuned during training. For example, training datacan be provided to the input layer 1202 of the neural network 1200, andthe neural network 1200 can use the training data to tune one or morenumeric weights of the neural network 1200. In some examples, the neuralnetwork 1200 can be trained using backpropagation. Backpropagation caninclude determining a gradient of a particular numeric weight based on adifference between an actual output of the neural network 1200 and adesired output of the neural network 1200. Based on the gradient, one ormore numeric weights of the neural network 1200 can be updated to reducethe difference, thereby increasing the accuracy of the neural network1200. This process can be repeated multiple times to train the neuralnetwork 1200. For example, this process can be repeated hundreds orthousands of times to train the neural network 1200.

In some examples, the neural network 1200 is a feed-forward neuralnetwork. In a feed-forward neural network, every neuron only propagatesan output value to a subsequent layer of the neural network 1200. Forexample, data may only move one direction (forward) from one neuron tothe next neuron in a feed-forward neural network.

In other examples, the neural network 1200 is a recurrent neuralnetwork. A recurrent neural network can include one or more feedbackloops, allowing data to propagate in both forward and backward throughthe neural network 1200. This can allow for information to persistwithin the recurrent neural network. For example, a recurrent neuralnetwork can determine an output based at least partially on informationthat the recurrent neural network has seen before, giving the recurrentneural network the ability to use previous input to inform the output.

In some examples, the neural network 1200 operates by receiving a vectorof numbers from one layer; transforming the vector of numbers into a newvector of numbers using a matrix of numeric weights, a nonlinearity, orboth; and providing the new vector of numbers to a subsequent layer ofthe neural network 1200. Each subsequent layer of the neural network1200 can repeat this process until the neural network 1200 outputs afinal result at the output layer 1206. For example, the neural network1200 can receive a vector of numbers as an input at the input layer1202. The neural network 1200 can multiply the vector of numbers by amatrix of numeric weights to determine a weighted vector. The matrix ofnumeric weights can be tuned during the training of the neural network1200. The neural network 1200 can transform the weighted vector using anonlinearity, such as a sigmoid tangent or the hyperbolic tangent. Insome examples, the nonlinearity can include a rectified linear unit,which can be expressed using the following equation:

y=max(x, 0)

where y is the output and x is an input value from the weighted vector.The transformed output can be supplied to a subsequent layer, such asthe hidden layer 1204, of the neural network 1200. The subsequent layerof the neural network 1200 can receive the transformed output, multiplythe transformed output by a matrix of numeric weights and anonlinearity, and provide the result to yet another layer of the neuralnetwork 1200. This process continues until the neural network 1200outputs a final result at the output layer 1206.

Other examples of the present disclosure may include any number andcombination of machine-learning models having any number and combinationof characteristics. The machine-learning model(s) can be trained in asupervised, semi-supervised, or unsupervised manner, or any combinationof these. The machine-learning model(s) can be implemented using asingle computing device or multiple computing devices, such as thecommunications grid computing system 400 discussed above.

Implementing some examples of the present disclosure at least in part byusing machine-learning models can reduce the total number of processingiterations, time, memory, electrical power, or any combination of theseconsumed by a computing device when analyzing data. For example, aneural network may more readily identify patterns in data than otherapproaches. This may enable the neural network to analyze the data usingfewer processing cycles and less memory than other approaches, whileobtaining a similar or greater level of accuracy.

Some machine-learning approaches may be more efficiently and speedilyexecuted and processed with machine-learning specific processors (e.g.,not a generic CPU). Such processors may also provide an energy savingswhen compared to generic CPUs. For example, some of these processors caninclude a graphical processing unit (GPU), an application-specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), anartificial intelligence (Al) accelerator, a neural computing core, aneural computing engine, a neural processing unit, a purpose-built chiparchitecture for deep learning, and/or some other machine-learningspecific processor that implements a machine learning approach or one ormore neural networks using semiconductor (e.g., silicon (Si), galliumarsenide(GaAs)) devices. Furthermore, these processors may also beemployed in heterogeneous computing architectures with a number of and avariety of different types of cores, engines, nodes, and/or layers toachieve various energy efficiencies, processing speed improvements, datacommunication speed improvements, and/or data efficiency targets andimprovements throughout various parts of the system when compared to ahomogeneous computing architecture that employs CPUs for general purposecomputing.

FIG. 13 illustrates a block diagram of a system 1300 in at least oneembodiment of the present technology.

System 1300 comprises a computing system 1380 configured to receive aprogram 1340 from another device in the system 1300 (e.g., a programgenerated by client 1360 or written by a user of client 1360). Forinstance, in one or more embodiments the computing system 1380 comprisesa server 1390 with a communication interface 1310 configured tocommunicate with other devices in the system 1300 (e.g., a client 1360).For instance, the server 1390 is configured to receive a program 1340specifying computer operations. The program 1340 can be received usingcommunication interface 1310 of the server 1390 (e.g. via wired orwireless communication). For instance, a device in the system 1300 maysend the program 1340 to the computing system 1380 to execute one ormore operations of the program 1340 on stored data 1322 in the computingsystem 1380. For example, the computing system 1380 may be a computingsystem capable of partitioning data and executing instructions inparallel (e.g., an Apache Spark® server has become one of the mostpopular and powerful platforms for distributed, in-memory parallelprocessing).

In one or more embodiments, the computing system 1380 is configured toprovide (e.g., using the communication interface 1310) program output1350 to one or more devices of the system 1300 (e.g., client 1360). Forinstance, the server 1390 is a computing device and configured toexecute computer operations related to program 1340 (e.g., control orbegin execution by one or more nodes of computing system 1380). Theserver 1390 can then provide program output 1350 indicating a result ofexecuting the program 1340. Alternatively or additionally, othercomputing devices 1384 or databases 1382 are employed to execute thecomputer operations and provide program output 1350. For instance, inone or more embodiments, the server 1390 itself comprises orcommunicates with one or more databases 1382 and/or other computingdevices 1384 (e.g., other servers) of computing system 1380. In suchcases, the computing system 1380 collectively represents or functions asa server 1390 for communicating with and/or executing a program fromanother device in the system 1300 (e.g., client 1360 or an applicationon client 1360). For example, the computing system utilizes adistributed data system where executing of a program is conducted bydifferent computing nodes (e.g., in parallel) or on data stored atdifferent databases within the distributed data system.

In one or more embodiments, the server 1390 has a computer-readablemedium 1320 and a processor 1330. Computer-readable medium 1320comprises one or more electronic holding places or storage forinformation, programs, or data to be accessed by processor 1330. Forinstance, the processor can cause programs or other data received usingcommunication interface 1310 to be stored in computer-readable medium1320. Computer-readable medium 1320 can include, but is not limited to,any type of random access memory (RAM), any type of read only memory(ROM), any type of flash memory, etc. such as magnetic storage devices(e.g., hard disk, floppy disk, magnetic strips), optical disks (e.g.,compact disc (CD), digital versatile disc (DVD)), smart cards, flashmemory devices, etc.

In one or more embodiments, processor 1330 executes instructions (e.g.,stored at the computer-readable medium 1320). The instructions can becarried out by a special purpose computer, logic circuits, or hardwarecircuits. In one or more embodiments, processor 1330 is implemented inhardware and/or firmware. Processor 1330 executes an instruction,meaning it performs or controls the operations called for by thatinstruction. For instance, in one or more embodiments, processor 1330executes an instruction by performing on controlling operations on oneor more nodes of computing system 1380. The computing system 1380 maycomprise one or more of databases 1382, one or more of computing devices1384 and/or server 1390. For instances, the operations may compriseoperations on stored data 1322 stored at the server 1390 or elsewhere inthe computing system 1380 (e.g., database 1382).

The instructions stored at the computer-readable medium 1320 can bewritten using one or more computer or programming language, scriptinglanguage, assembly language, etc. Processor 1330 in one or moreembodiments can retrieve a set of instructions from a permanent memorydevice and copy the instructions in an executable form to a temporarymemory device that is generally some form of RAM, for example.

In one or more embodiments, the computer-readable medium 1320 storesinstructions for execution by processor 1330. For instance, in one ormore embodiments, the server 1390 stores the program 1340 in thecomputer-readable medium 1320 or elsewhere in the computing system 1380.In one or more embodiments, the server 1390 generates for the program1340 alternative instructions (e.g., computer-generated and/or in acomputing language different than program 1340). For example,computer-readable medium 1312 comprises instructions 1324 to generatecomputer instructions and comprises instructions 1324 to executecomputer instructions (e.g., for execution by nodes or devices ofcomputing system 1380 of received or generated computer instructions).For instance, in one or more embodiments the received program is in acomputer language not useful or convenient for execution by one or morenodes or devices of computing system 1380, and the server 1390 generatescomputer-generated computer instructions in a different computinglanguage.

In one or more embodiments, the program 1340 comprises one or morecomponents (e.g., thread program component 1342 or data programcomponent 1346) and information (e.g., partitioning information 1344 and1348). For instance, in one or more embodiments, the program 1340comprises a thread program component 1342 that comprises a sequence ofcomputer instructions that can be executed independently. For example, aprogram could have multiple thread components. In one or moreembodiments, a particular thread program component is capable forexecution in parallel on different partitioned data. Then the computingsystem 1380 (e.g., the server 1390) can partition the stored data 1322and group it at particular nodes of the computing system such that eachnode executes instructions of the thread program component on its subsetof data. In one or more embodiments, the program 1340 does not specifypartitioning information 1344 for partitioning and/or grouping thestored data 1322, and the computing system 1380 uses its own criteria topartition and group the stored data 1322 (e.g., based on loadconsiderations of nodes within the computing system). In one or moreembodiments, the program 1340 does specify partitioning information 1344for partitioning and/or grouping the stored data 1322 (e.g., using a BYoperation), and the computing system 1380 considers this criteria and/orits own criteria to partition and group the stored data 1322.

In one or more embodiments, other devices in the system 1300 alsocomprise a communication interface for communicating via wired orwireless communication with devices in the system 1300 (e.g.,communication interface 1364). Other devices may also have a processorand computer-readable medium with similar features as described withrespect to processor 1330 and computer-readable medium 1320. Forinstance, in one or more embodiments, the computer-readable medium 1366stores instructions for execution by processor 1362 to generate program1340 (e.g., generate program application 1368).

In one or more embodiments, the program 1340 has a data programcomponent 1346 that comprises computer instructions for operations onthe output of the thread program component 1342. In a simple example,the operations comprise instructions for staging the output of thethread program component (e.g., a SET operation or an OUTPUT operation).In other cases, the operations comprise more complex operations on theoutput of the thread program component (e.g., arithmetic operations,conditional operations, relational operations, logical operations, andassignment operations). In this case, it may be advantageous topartition and group the output of the thread operations and distributethem on nodes of the computing system. In one or more embodiments, theprogram 1340 does not specify partitioning information 1348 forpartitioning and/or grouping the output of executing the thread programcomponent 1342, and the computing system 1380 uses its own criteria topartition and group the output (e.g., based on load considerations ofnodes within the computing system). In one or more embodiments, theprogram 1340 does specifies partitioning information 1348 forpartitioning and/or grouping the output (e.g., using a BY operation),and the computing system 1380 considers this criteria and/or its own topartition and group the output.

In one or more embodiments, the program is a user-written program. Inone example, the user-written program is a user-written DS2 programusing PROC DS2 programing language provided by SAS Institute Inc. ofCary, N.C. In another example, a user-supplied program (e.g., in aSCALA™ programming language) may come from one server (e.g., using aSAS® Cloud Analytics Services (CAS)) to be executed inside anotherserver (e.g., a SPARK-compatible server). For instance, this may occurwhen data is stored on one server (e.g., a SPARK-compatible server), butthe program comes from another server (e.g., a CAS server). In thiscase, computer-generated computer instructions can be generated inresponse to a received request from a server to execute a program ondata stored at another server.

In one or more embodiments, the program could itself becomputer-generated or computer-generated responsive to a user request.For example, an action to load or save a table (e.g., a CAS action orSAS® Viya Parallel Data Connector action), a request to run a scoringmodel (e.g., using the Scoring Accelerator macro or SAS Viya PROCSCOREACCEL), and/or running operations related to a Data StepAccelerator, Code Accelerator and Scoring Accelerator could generate ortrigger computer instructions.

One or more embodiments enable executing the program in a computingsystem that may not be able to easily interpret or compile the receivedprogram (e.g., using an embedded process in a Spark® server).

FIG. 14 illustrates a method 1400 for executing a program (e.g., aprogram 1340). For example the method 1400 can be implemented by acomputing system (e.g., computing system 1380) and/or a server (e.g.,server 1390).

The method 1400 comprises an operation 1401 that includes receiving, ata server of a computing system, a program (e.g., program 1340)specifying computer operations on stored data. For example, the programcould be received from a client remote from the server and/or acomputing system of the server. The program is in a first computerlanguage (e.g., in a DS2 programing language). For example, the firstcomputer language could be a text-based or machine-based computerlanguage. The computer language could be user-written computerinstructions (e.g., written by a user of client 1360) or it could begenerated by a machine (e.g., generated by client 1360). The computingsystem is configured to partition the stored data into multiple sets ofpartitioned data for performing parallel execution of one or more of thecomputer operations on each of the multiple sets of partitioned data.

The method 1400 comprises an operation 1402 that includes determiningwhether the program comprises a thread program component. Threadoperations of the thread program component comprise computerinstructions for execution in parallel of the one or more of thecomputer operations on each of the multiple sets of partitioned data.

The method 1400 comprises an operation 1403 that includes responsive todetermining that the program comprises a thread program component,generating, at the server, computer-generated computer instructions forexecuting the one or more of the computer operations in parallel. Thecomputer-generated computer instructions are in a second computerlanguage different than the first computer language (e.g., a computerlanguage provided by JAVA®, PYTHON™, R™ or SCALA™). For example, thefirst computer language could be a computer language readable by aclient and the second computer language is in a computer languagereadable by one or more entities in the computing system.

The computer-generated computer instructions are dependent on one ormore criteria. For instance, the particular instructions selected orarranged could be based on one or more criteria. The criteria couldinclude whether the thread program component specifies data keyinformation for partitioning and grouping the stored data using a firstkey indicated by the data key information. Additionally, oralternatively the criteria includes whether the program comprises a dataprogram component comprising data program instructions for operationscapable of execution in parallel on output data that is output fromexecution of the thread program component. Additionally, oralternatively the criteria includes whether the data program componentspecifies output key information for partitioning and grouping theoutput of the thread program component using a second key indicated bythe output key information.

The method 1400 optionally comprises an operation 1404 that includesexecuting, by the server, the program according to thecomputer-generated computer instructions. Executing in this regard couldinclude initiating execution at one or more nodes of a computing system(e.g., computing system 1380). Additionally or alternatively executingcould comprise partitioning or grouping data according to thecomputer-generated computer instructions. Additionally or alternatively,executing comprises executing the operations at the server on datastored at the server.

As an example, operation 1404 includes requesting the computing systemto partition stored data (e.g., data stored at a server in the computingsystem) into multiple sets of the partitioned data. As another example,operation 1404 includes requesting the computing system to distribute arespective one of the multiple sets of the partitioned data torespective ones of different computing nodes of the computing system forexecuting computer operations of the program.

The method 1400 optionally comprises an operation 1405 that includestransmitting a result of executing the program (e.g., the server 1390transmits program output 1350 to client 1360. Embodiments describedherein are applicable to other systems and protocols, and examplesystems described herein should not be construed as limiting theapplicability of embodiments to other systems.

FIG. 15 illustrates a block diagram of a computing system 1380comprising a server 1390. In this example, the server 1390 hasclient-side or client-facing computer architectural components. Thesecomponents can be collectively referred to as an embedded process inthat it is embedded on a server 1390. For example, one type of server isa Spark-compatible server (or Spark® server). A Spark® server is used asan example server because it has become one of the most popularplatforms for distributed, in-memory parallel processing. A Spark®server can be useful for low cost, fast, and efficient massivelyparallelized data manipulation. Embodiments described herein areapplicable to other types of servers. For example, embodiments areapplicable to servers that provide a combination of libraries thatallows database query (e.g., using Structured Query Language, SQL™),event stream processing, machine learning, and/or graph processing.Additionally or alternatively, embodiments are applicable to serversthat process massive amounts of data stored on a cluster of commodityhardware (e.g., providing an open-source parallel processing framework).

In one or more embodiments, the server 1390 comprises a Base 1510 forexecuting processes associated with another computing system. Forexample, SAS® Embedded Process (EP) is a portable, lightweight executioncontainer that allows the parallel execution of SAS processes insideHadoop®, Spark®, Teradata®, and many other massive parallel processing(MPP) databases. In one or more embodiments, the Base 1510 is acontroller or comprises a controller (e.g., a CAS controller).

SAS® Embedded Process is sufficient to support the multi-threaded SAS®proprietary DS2 language. DS2 is a procedural programming languageinfluenced by the SAS® proprietary DATA step language. The DS2 languageexcels at achieving parallel execution. Most DATA step functions can becalled from a DS2 program. DS2 programs can run in the Base SAS languageinterface using PROC DS2, SAS® High-Performance Analytics, SAS®In-Database Scoring Accelerator, SAS® In-Database Code Accelerator, andSAS® Viya® Cloud Analytic Services.

In one or more embodiments, the server 1390 comprises an EmbeddedProcess Client Interface 1500 (e.g., to communicate with a remote clientto receive a program described herein). For instance, the program can bewritten in a language specific to the language supported by an embeddedprocess described herein. The Embedded Process Client Interface 1500 maybe configured to receive execution requests.

The parallel syntax of the DS2 language coupled with SAS EmbeddedProcess allows traditional developers utilizing SAS® products to createportable algorithms that are implicitly executed inside computingsystems using other technology (e.g., Hadoop® MapReduce and ApacheSpark®). For instance, running or executing DS2 code directly inside aSpark® server effectively leverages the massive parallel processing andnative resources, rather than moving the data from the computing systemto a client. Applying the process or code to the data eliminates datamovement and decreases overall processing time.

SAS® Embedded Process is a part of SAS® In-Database Technology. In sucha context, SAS® In-Database Technologies offers a flexible, efficientway to leverage increasing amounts of data by injecting the processingpower of SAS® wherever the data lives (e.g. using a SAS® EmbeddedProcess). SAS® In-Database Technologies can tap into the massivelyparallel processing (MPP) architecture of Apache Hadoop® and ApacheSpark® for scalable performance. SAS® In-Database Code Accelerator forHadoop allows the parallel execution of user-written DS2 programs usingApache Spark.

In one or more embodiments, an execution request received or process bythe Embedded Process Client Interface 1500 could consist of an arbitraryprogram supplied by the user or a program generated by the EmbeddedProcess Client Interface 1500 (e.g., a SCALA™ program) or anothermachine. Program can be generated when a user submits a CAS action—suchas a request to load and save a table or a request to run score code—tobe executed inside another server (e.g., a Spark-compatible server).Programs can also be generated when running SAS In-Databaseprocesses—such as Code Accelerator, Scoring Accelerator and Data StepAccelerator—inside another server (e.g., a Spark-compatible server). Agenerated program can be in a language interpretable or compliable bythe server 1390 (e.g., a SCALA™ computer language). The receivedarbitrary program could be a program comprising computer instructionsthat are directly implemented by the server without generatingadditional computer-generated computer instructions related tocompliance with the server. The received arbitrary program could beresponsive to a user request or action at a server remote from thecomputing system 1380.

In one or more embodiments, programs are started from Base 1510. Forexample, SAS® In-Database Code Accelerator DS2 programs are started fromBase SAS® using PROC DS2. When the execution platform indicates thatanother platform is being used (e.g., the platform is set to SPARK®),the Embedded Process Client Interface 1500 can generate a program in aplatform specific language (e.g., a SCALA™ program). For instance, theserver 1390 can interpret or compile the text-based computerinstructions to machine language before execution.

The Embedded Process Launcher 1520 deploys the user-written code (e.g.,DS2 or SCALA™) and the generated code (e.g., SCALA™ programs) to thecluster and submits the SAS Embedded Process Spark application forexecution. For example the server 1390 can control executing by acomputing cluster 1550. The computing cluster 1550 can comprise variouscomputing nodes 1560 for partitioning and grouping data onto thecomputing nodes (e.g., computing nodes of computing system 1380).

The Embedded Process Launcher 1520 can also be used to start-up and shutdown instances of an Embedded Process Continuous Session (EPCS). Forexample, a SAS® Spark® EPCS is an instantiation of a long-lived EmbeddedProcess session on a particular cluster capable of serving one SAS Viya®Cloud Analytics Services session or one Base SAS session.

EPCS can process multiple execution requests without having to start andstop every time an execution request is made. For example, EPCS can beused to perform multiple SAS Viya Parallel Data Connector actions withinthe same session, such as save and load a table; run a scoring model,etc. EPCS can be used to run Data Step Accelerator, Code Accelerator andScoring Accelerator.

In one or more embodiments, by bringing together data storage (e.g., ofbig data), processing power of servers and databases like Hadoop® andSpark®, and the intelligence of SAS® products, a user can achievegreater storage capacity, greater parallel processing capabilities,and/or faster data growth and processing time. For example, a user canwrite more complex algorithms to obtain more precise results. One ormore embodiments may also be helpful for data management and integrationin order to promote broad reuse while being compliant with InformationTechnology (IT) policies and procedures; and/or boost the value ofanalytics infrastructure while reducing the cost to maintain it.

In one or more embodiments, the server 1390 comprises a SessionManagement component 1512 and the Embedded Process Client Interface 1500connects to a running instance of the EPCS, using the Session Managementcomponent 1512, and submits execution requests, receives executionresponses and application events, such as task start/stop.

EPCS offers a tight integration between CAS and Spark® by allowinguser-supplied SCALA™ programs submitted from CAS to be executed inside aSpark-compatible server. SCALA™ programs can be submitted as plain textand are compiled and executed by an embedded process described herein.SCALA™ programs can be compiled into a class and executed as a singleunit of work or can be interpreted and executed just like in a Shellenvironment, where SCALA™ variables can be accessed by subsequentexecutions of SCALA™ code.

Users may run an arbitrary SCALA™ program that loads a Spark® Datasetinto the Spark® memory and later on run a CAS action to load thein-memory Dataset into CAS. Users may also save a CAS table in Spark® asan in-memory Dataset and then apply an arbitrary SCALA™ program toprocess the data. Allowing arbitrary SCALA™ programs to be submitted andexecuted in a Spark-compatible server creates an integrated processingframework between CAS and a Spark-compatible server.

In one or more embodiments, the Embedded Process Launcher 1520 launchesa driver program which runs in a driver container (e.g., at a givencomputing node 1560 of the computing cluster 1550). One or moreembodiments, are useful for keeping data secure because the data doesnot need to leave the computing cluster, but instead computer programscan be executed on the data within a computing cluster (e.g., computingcluster 1550).

One or more embodiments are also compatible with Apache Spark® serverswho are running on or with Apache Hadoop® technology (e.g., Yet AnotherResource Negotiator, YARN). YARN is a large-scale, distributed operatingsystem for big data applications. For instance, the embedded processcomponents can be installed on every node of a cluster running a Spark®task. The computing resources used by the embedded process can then bemanaged by YARN.

FIG. 16 illustrates a block diagram of a driver container 1600 executedin the computing cluster 1550. For example, arbitrary programs orgenerated programs (e.g., in a SCALA™ program language) can be executedinside a driver container 1600 (e.g., a Spark® Driver container, wherethe Spark® Embedded Process driver program runs).

Upon submission of an application to a server (e.g., a program describedherein is submitted to a Spark-compatible server), or an EPCS launch, adriver container 1600 and one or more executor containers 1650 can beallocated in a computing cluster 1550. As an example, the drivercontainer is capable of running a Spark® YARN Application Master processwhere the main (or session) driver program runs.

As shown in FIG. 16, a Main Driver 1604 can receive an execution request(e.g., from Embedded Process Launcher 1520). The execution request couldcomprise generated code in response to a received program as describedherein. Alternatively, or additionally, the execution request comprisesan arbitrary program readable by the server (e.g., an arbitrary SCALA™code). The Main Driver 1604 can optionally request that the generatedcode be interpreted and/or compiled by an Interpreter/Compiler 1608. Inone or more embodiments, the generated code is generated in a computerlanguage that can be executed without compiling an entire program beforeexecuting any computer instructions in the generated code. For example,if the execution request contains generated SCALA™ program code, theSCALA™ program can be interpreted or compiled during execution (i.e. onthe fly) by the Interpreter/Compiler 1608. A computing system can beginexecuting a program without pre-compiling all of the generated code.

In one or more embodiments, the creation of a Generated Driver 1606 istriggered. The generated driver 1606 can be the product of the generatedprogram compilation. The compilation can be stored into a class that isstored in a JAVA® Virtual Machine class loader. The Generated Driverdrives the execution of an application of an embedded process on theserver (e.g., a SAS® Embedded Process Spark® application).

The Generated Driver 1606 can create a context 1610, instantiate anembedded process function 1620, and/or dispatch information to executivecontainers 1650 (e.g., on other nodes of the computing cluster 1550).

In one or more embodiments, the Main Driver 1604 puts the GeneratedDriver 1606 into execution by calling its drive( ) method. Inside thedrive( ) method, the Generated Driver creates a Context 1610 (e.g., aSAS® Context), which is responsible for an early compilation of aprogram received by a server described herein (e.g., a user-written DS2program) and for collecting and storing all necessary information to runthe program when it is dispatched to the Executor Containers 1650. Forinstance, the Context 1610 comprises a Task Context 1612 (e.g., a SAS®Context for holding a SAS® Embedded Process Task Context), Input/Output1616 (e.g., an input and output file or table metadata or INPUT OUTPUTHDMD), and an Output Encoder Object (ENC) 1614.

In the same or different embodiments, an arbitrary program component maynot trigger the creation of a Generated Driver 1606 because the programcan be executed directly.

In one or more embodiments an embedded process task controller 1640 isused to maintain a connection between a driver container 1600 and anexecutor container 1650.

In one or more embodiments, a computing cluster (e.g., computing cluster1550) is used for a Spark® server running a SAS Embedded Processconsisting of a Spark® driver program that runs in the Spark®application master container (e.g., driver container 1600) and a set ofspecialized functions that run in the Spark executors' containers (e.g.,executor containers 1650).

In one or more embodiments, input in the Input/Output 1616 comes fromreading data from a Hadoop® Distributed File System (HDFS) file. Theinput metadata can be, for example, passed by PROC DS2 duringapplication submission or as part of an execution request describedherein. When reading data from a Hive table, the input metadata isretrieved from a server's dataset schema (e.g., returned from a SQLTMstatement execution request). Task Context 1612 creates the outputmetadata (e.g., Hadoop® metadata, HDMD) based on the early compilationof the user-written DS2 program inside the Embedded Process NativeInterface 1602. The output metadata is used to create the Output EncoderObject 1614. The Output Encoder Object 1614 is used to create the schemaof the output Distributed Data Set (DDS) that comes out of the EmbeddedProcess Function 1620. For instance, types of DDS include a Dataset orRDD (Resilient Distributed Dataset).

In one or more embodiments, a specialized Embedded Process Function 1620is instantiated by the Generated Driver 1606 and applied to a DDS (e.g.,a Spark® Dataset or RDD). There are many types of specialized EmbeddedProcess functions. For instance, their instantiations can depend on themany different ways to run SAS® In-Database Code Accelerator inside aSpark-compatible server.

The driver program can be written in one or more computer languages. Oneexample driver program is a SAS® Embedded Process Spark Main Driverprogram. The driver program according to this example can havecomponents that are written in C language where all the interactionswith an execution container happen (e.g., according to a DS2 code). Thedriver program can also have components written in JAVA® and/or SCALA™computer language where interactions with the Spark® environment happenbecause these are language readable by a Spark® server. For instance,JAVA® code can be used for extracting data from Hive tables or HDFSfiles and passing them to an execution container (e.g., a DS2 executioncontainer). The SCALA™ code can drive the Spark® application. C, JAVA®,and SCALA™ code can run on the same JAVA® Virtual Machine (JVM) processthat is allocated by the Spark application master and executors'containers. In order to eliminate multiple copies of the input data,JAVA® and C code can access shared memory buffers allocated by nativecode. In order to minimize JAVA® garbage collections, the shared nativebuffers can be allocated outside of the JVM heap space. Shared nativememory allocations and CPU consumption can be managed by YARN resourcemanagement and can be compliant with resource constraints imposed byYARN. Journaling messages generated by C and DS2 code components of thedriver program can be written to the Spark standard output (stdout) orstandard error (stderr) logs. Messages generated by the JAVA® and SCALA™code can be written to the Spark application log (syslog). Further, thedriver program can comprise other languages (e.g., a PYTHON® or RTMcomputer languages).

In one or more embodiments, the driver container contains othercomponents (e.g., an application event listener 1630). An applicationevent listener 1630 can be used to receive notifications from anexecutor container 1650 when a task is started or ended.

FIG. 17A illustrates a block diagram of an Executor Container 1650 forinput partition data 1700A grouped from stored data 1322. There could beother partitioned data (e.g., 1700B and 1700N) processed in otherexecutor containers. Each executor container is executing a given taskor unit of work. There could be several tasks within an application. Forinstance, there could be one or more stages in a given task that are aset of tasks that depend on each other.

In one or more embodiments, a task manager 1710 manages the execution ofa given task (e.g., the execution of a specialized Embedded ProcessFunction 1720 instantiated by the generated driver 1606). For instance,Spark® tasks run in the executor container process. Each task isassigned an input partition, which might come, for example, from a Hivetable or an HDFS file. Each task produces an output partition 1770. Whenall tasks are finished, the Generated Driver 1606 sees all outputpartitions as a single abstract unit called output Distributed Data Set(DDS) (e.g., an RDD). The output DDS in a Spark® Server can be persistedto a Hive table or to an HDFS file or remain in Spark memory for furtheruse.

FIG. 17B illustrates a flow diagram for a method implemented in anExecutor Container 1650 for producing an output partition. In anoperation 1791, the Embedded Process Function 1720 creates the inputchannel 1730 and output channel 1760. The Embedded Process Function 1720is also responsible for controlling the allocation and de-allocation ofone or more containers 1750 (e.g., a DS2 container).

In an operation 1792, Embedded Process Function 1720 retrieves recordsfrom the input partition data 1700A and pushes them into a container1750 through the input channel 1730 and Embedded Process Input Driver1752. Input data are serialized in a format that is understood by theprogram of the container (e.g., a DS2 program) and stored in nativeinput buffers. The Embedded Process Native Interface 1740 is thefrontier between the language of the Container 1750 and the language ofthe Executor Container 1650 (e.g., a JAVA® and/or C code language).

In an operation 1793, the Container 1750 obtains the serialized inputrecords from the Embedded Process native input buffers through theEmbedded Process Input Driver 1752 and processes them.

In an operation 1794, Container 1750 outputs and stores one or morerecords in output native buffers through the Embedded Process OutputDriver 1754.

In an operation 1795, output channel 1760 retrieves output records fromnative output buffers. Using the Output Encoder Object 1724, the outputchannel 1760 serializes the records in a format understood by a server(e.g., a server described herein). Serialized output records are storedin the Output Partition 1770.

Method 1790 is merely an example; operations or sub-operations could becompleted in a different order then specified in method 1790.

One or more embodiments described herein are applicable for use of aSAS® In-Database Code AcceleratorTM to implement a combination ofgenerated SCALA™ programs, Spark® SQL statements, HDFS files access, andDS2 programs on a Spark® server.

Parallel execution of SAS In-Database Code Accelerator inside the SASEmbedded Process on Spark consists of one Spark application. A Sparkapplication consists of one or more jobs. Jobs consist of one or morestages that are a set of tasks that depend on each other. By generatingSCALA™ programs that integrates with the SAS Embedded Process programinterface in a Spark server, the many phases of a SAS In-Database CodeAccelerator job can be comprised of one single Spark job.

The degree of parallelism implemented by a computing system depends onhow the data is partitioned. Therefore, the number of tasks depends onthe number of input data partitions. For instance, a Spark® serverassigns one partition per task. The number of parallel tasks depends onthe number of available executors, the number of cores per executor, andthe number of cores per task. There are many performance tuningproperties that can be used to control the application execution. Forexample, some tuning properties may be set when EPCS is started.Additional tuning properties can include Spark® properties set in thespark-default.conf configuration file. Examples of such Sparkconfiguration properties are:

-   -   (1) spark.executor.instances: specifies the number of executors        allocated per application;    -   (2) spark.executor.cores: specifies the number of cores        allocated per executor; and    -   (3) spark.task.cpus: specifies the number of cores to allocate        for each task. SAS® Embedded Process on Spark® provides two sets        of specialized functions that are capable of reading data from        an input partition and applying them to the DS2 program:    -   (1) File functions: applied when the input data is read from a        file stored in HDFS; and    -   (2) Dataset functions: applied when the input data is read from        a table stored in Hive.

The functions are categorized as transformations or actions.Transformation functions consume data from a DDS (e.g., a Dataset orRDD) and produce another DDS (e.g., a Dataset or RDD). Action functionsconsume data from a DDS (e.g., a Dataset or RDD) and write the outputdata directly to a file stored in HDFS.

SAS® In-Database Code Accelerator™ for Hadoop® enables the publishing ofuser-written DS2 thread or data programs to Spark®, where they can beexecuted in parallel exploiting the massively parallel processing powerof a Spark® server. Examples of DS2 thread programs include largetranspositions, computationally complex programs, scoring models, andBY-group processing.

To use Spark® as the execution platform, the DS2ACCEL option in the PROCDS2 statement is set to YES or the DS2ACCEL system option is set to ANY;the HADOOPPLATFORM system option is set to SPARK; the Hive table or HDFSfile used as input resides on the cluster; and SAS® Embedded Process isinstalled on all the nodes of the Hadoop® cluster that are capable ofrunning a Spark® Executor.

Data is distributed on different nodes of the computing cluster. Forinstance, different databases can be used for storing and distributingdata (e.g., sequel databases). There are several providers of databaseproducts and services for storing and distributing data including thoseidentified by SQL™, Natisa™, SAP HANA®, Oracle®, Teradata®, and DB2.

The server breaks down the input data into distributed data set (DDS)partitions. For instance a Spark-compatible server breaks down inputfrom a table into a Dataset and input from file into a RDD partition.Data partitions are also known as file blocks or file splits. Eachpartition is assigned to a Spark task, where the DS2 program runs. EachDS2 program has access to its own data partition.

Embodiments herein are applicable to other servers and computing systemsbeside Spark-compatible servers. However, when running complexuser-written DS2 programs inside other database systems, executing aSAS® In-Database Code Accelerator™ may require multiple phases. Forexample: when running inside Hadoop® MapReduce, SAS® In-Database CodeAccelerator™ exploits the map and reduce phases in order to get to thefinal result; in some cases, multiple MapReduce jobs may be required.

In one or more embodiments, CAS actions are used to provide CAS actionsto a server described herein (e.g., server 1390). For example CASSPARKEPis an action set that provides CAS actions to interact with the SAS®Spark Embedded Process Continuous Session. STARTSPARKEP (Start EmbeddedProcess Action), starts the SAS® EPCS on a particular cluster of acomputing system (e.g., computing system 1380). The action can take oneor more of the following parameters including other parameters notlisted here:

-   -   1) username: specifies the Hadoop® user name that owns the EPCS        Spark application.    -   2) password: specifies the Hadoop® user's password.    -   3) trace: specifies if the Embedded Process will run with        traces.    -   4) timeout: specifies the number of seconds the Spark® Embedded        Process waits for execution requests. If the timeout period is        reached the Embedded Process terminates.    -   5) properties: specifies a list of additional Spark® or Hadoop®        configuration properties and their values. Property name and        value are separated by ‘=’ sign.    -   6) classpath: specifies the class path used in the Hadoop® call        context. The class path contains a folder or individual JAR        files. The Hadoop® configuration folder is included in the class        path if, for instance, the configpath argument is not specified.        This argument is required if the caslib argument is not        specified.    -   7) configpath: specifies a single folder where all the Hadoop        and Spark configuration files reside. When the configpath        argument is specified, the configuration folder does not need to        be added to the classpath argument. This argument is required if        the caslib argument is not specified.    -   8) executorinstances: specifies the number of Spark® executors        allocated per Embedded Process instance.    -   9) executorCore: specifies the number of cores per Spark        executor.    -   10) executorMemory: specifies the maximum amount of memory,        e.g., in gigabytes (GB), that can be allocated by a Spark®        executor (e.g. 2, 8).    -   11) taskCores: specifies the number of cores allocated for each        task.    -   12) customJar specifies a list of local folders containing        files, individual JAR files or individual files that are added        to the application distributed cache. Folders and files must be        separated by “,” or “:” or “;” or “ ” (space).

The arguments username, password, classpath and configpath overwritewhat was specified in CAS Library data source argument.

The following LUA example starts the Embedded Process on a cluster usingthe data source arguments provided by the CAS Library hive:

session:startsparkep { caslib=“hive”,  executorInstances=4,executorCores=2,  executorMemory=16, taskCores=1  }

The following LUA example starts the Embedded Process on a clusterwithout specifying the data source options from a CAS Library:

session:startsparkep { username=″daghaz″,  classpath=”/tmp/hadoopjars”, configpath=”/tmp/hadoopcfg”,  executorInstances=4, executorCores=2, executorMemory=16, taskCores=1,  properties={″a=b″, ″c=d″},  timeout=60 }

FIG. 18 illustrates an example program and program indicators. In one ormore embodiments, a computing system parses a program and sets one ormore indicators (e.g., indicators 1820, 1822, 1824, and 1826) forpossible features of a program (e.g., features 1810, 1812, 1814, and1816). For example, FIG. 18 shows a program 1890 for calculating anaverage manufacturer's suggested retail price (MSRP) from stored vehicleinformation. The computing system determines whether a program comprisesa thread program component feature 1810 (e.g., thread program component1832). An indicator 1820 (e.g., a first indicator) is set indicatingwhether the program comprises the thread program component. In thiscase, the program 1890 comprises a thread program component, so thefirst indicator 1820 is set to indicate the presence of this component.

Additionally, or alternatively, the computing system determines whethera program comprises a data program component feature 1814 (e.g., dataprogram component 1836). An indicator 1824 (e.g., a second indicator) isset indicating whether the program comprises the data program component.In this case, the program 1890 comprises a data program component, sothe second indicator 1824 is set to indicate the presence of thiscomponent.

Additionally, or alternatively, the computing system determines whethera thread program component comprises a feature 1812 specifyinginformation for partitioning and grouping stored data (e.g., theinstruction 1830). An indicator 1822 (e.g., a third indicator) is setindicating that the thread program component specifies the informationfor partitioning and grouping the stored data using the first key. Forinstance, in the program 1890, the thread program component 1832includes an instruction 1830 (a BY statement) for partitioning andgrouping the data by car make type using a key related to car make type.In one or more embodiments, a computing system determines whether thethread program component specifies information for partitioning andgrouping by parsing the thread program component for a BY statement.

Additionally, or alternatively, the computing system determines whethera data program component comprises a feature 1816 specifying informationfor partitioning and grouping the output of the thread program component(e.g., the instruction 1834). An indicator 1826 (e.g., a fourthindicator) is set indicating whether the data program componentspecifies the information for partitioning and grouping the output ofthe thread program component using the second key. In one or moreembodiments, a computing system determines whether the data programcomponent specifies information for partitioning and grouping by parsingthe data program component for a BY statement (e.g., instruction 1834).In this case, the output of the thread program component 1832 is alsopartitioned and grouped by car make type using a key related to car maketype indicated by a BY statement. However, in other embodiments,different keys could be used in the data program component and threadprogram component. Further, other types of indicators could be used thenshown here (e.g., flags or number values). In one or more embodiments,indicators are set in an order different than the order set here or notat all. Additional indicators could also be set to indicate otherinformation for generating computer instructions (e.g., computerinstructions readable by a Spark-compatible server).

FIGS. 19-24 indicate flow diagrams for generating components ofcomputer-generated computer instructions for a received program. Aprogram generated according to the flow diagram 1900 can be executed bya computing system (e.g., computing system 1380) or server (e.g., server1390) described herein. The examples are written with respect to aprogram written in a DS2 program, but are applicable to programs inother languages. The generation of the computer instructions in theseexamples (e.g., a SCALA™ program by the Embedded Process ClientInterface) depends on how a received program (e.g., a DS2 program) iswritten. The main factors considered in generating the code in theseexamples:

(1) Is there a thread program?

(2) Is there a thread program with a BY statement?

(3) Is there a data program with logic worth accelerating?

(4) Is there a data program with a BY statement?

For example, these factors could be indicated or considered by checkingone or more indicators described herein (e.g., indicators correspondingto features 1810, 1812, 1814, and 1816).

In regards to factor (3), this factor may relate to considering whethera received program has instructions for operations capable of executionin parallel on output of a thread program or acceleration. For instance,a simple SET statement or OUTPUT statement in the data program may nottrigger acceleration. Alternatively, other operators in a data programcomponent may indicate logic worth accelerating (e.g., instructionscapable of execution in parallel). For example, the data program maycomprise arithmetic operators indicating mathematical operations (e.g.,addition, subtraction, multiplication, and division) for executing ondata of the output of a thread program component. Additionally oralternatively, the data program may comprise conditional operators (e.g.operators that request a data program returning one value if thecondition is true and another if it is false). Additionally oralternatively, the data program may comprise relational operators usedto compare program variables. Additionally or alternatively, the dataprogram may comprise logical operators to perform logical operations onprogram variables (e.g., AND, OR, NOT statements). Additionally oralternatively, the data program may comprise assignment operators toassign values for program variables.

In one or more embodiments, a computing system determines whether theprogram comprises a data program component comprising the data programinstructions for the operations capable of execution in parallel on theoutput of the thread program component by parsing the program for one ormore of arithmetic operators, conditional operators, relationaloperators, logic operators, and assignment operators.

A received program can be executed on data retrieved from, for example,an electronic file type system or an electronic table. For example, thedata can be stored on a server receiving a program for executing on thedata. In one example embodiment, a Spark-compatible server provides theability to read HDFS files and query structured data from within aSpark® application. Wth Spark® SQL, data can be retrieved from a tablestored in Hive using an SQL statement and the Spark® Dataset ApplicationProgramming Interface (API). Spark® SQL provides ways to retrieveinformation about columns and their data types and supports the HiveQLsyntax as well as Hive SerDes (Serializers and De-serializers).

The generated code depends on how the received program (e.g., in a DS2language) is written. Six scenarios or cases are presented asnon-limiting examples.

-   -   (1) There is a thread program component with no BY statement;        there is no data program component.    -   (2) There is a thread and a data program component, none of them        with a BY statement.    -   (3) There is a thread program component with no BY statement and        a data program component with a BY statement.    -   (4) There is a thread program component with a BY statement and        no data program component.    -   (5) There is a thread program component with a BY statement and        a data program with no BY statement component.    -   (6) There is a thread and data program component, neither with a        BY statement.

FIG. 19A illustrates a flow diagram for a data table 1930 according toprogram indicators corresponding to case (1). An example DS2 threadprogram 1990 according to case (1) is shown in FIG. 19C with a threadprogram component 1992. This DS2 thread program 1990 runs in a singlephase and tasks are executed in parallel.

In one or more embodiments, a computing system determines there is athread program component (e.g., thread program component 1992) anddetermines whether the thread program component specifies informationfor partitioning and grouping the stored data using a first key. In thisexample, indicator 1820 indicates there is a thread program component.Indicator 1822 indicates the thread program component had no key providefor partitioning and grouping data (e.g., no BY statement).

In one or more embodiments, when the thread program component does notspecify information for partitioning and grouping the stored data usinga key, the computer-generated computer instructions are generated toextract the stored data from the electronic table (e.g., table 1930). Inthis example, a SPARK SQL operation 1932 is used to query a database andextract the stored data. However, other database operations or databasedproducts described herein could be used in examples describing a SPARKSQL operation. In this example, that data is extracted into a DDS thatis a single object record (DDS 1934). Alternatively, other types of DDScould be used for example, there is a kind of DDS that contains a keyfield and value field or (DDS<K,V>). The key field is for indexing dataof the value field. Another kind of DDS is one that utilizes rows(DDS<row>). An example DDS is a SPARK® Resilient Distributed Dataset(RDD).

For ease of explanation a particular kind of DDS is selected for each ofexamples described in FIGS. 19-24. However, it should be understoodeither of these kinds of DDS can be implemented in embodiments describedherein regardless of whether there is a key provided by a program ornot. For instance, when there is no BY statement, a key can be ignoredby assigning NULL to its value. Alternatively, the key is stored in theDDS<K,V> when there is a BY statement. Wth a DDS<row> there is alreadyno key associated with a row object. However, when there is a BYstatement, all the rows with the same key can be stored on the same DDS.Thus multiple DDS can be used to represent different data associatedwith different keys.

In one or more embodiments, a computing system determines whether theprogram has a data program component comprising data programinstructions for operations worth accelerating (e.g., capable ofexecution in parallel on the output of the thread program component). Inthis case, parsing the program 1990 would reveal a data programcomponent 1994 that contains a set and output statement for the outputof the thread and is deemed as not worth accelerating. In this case,where the program does not comprise the relevant data program component(i.e., one worth accelerating), the output of the thread programcomponent can be written directly to an electronic table 1950. In theexamples, shown in FIGS. 19-24, output is placed into a format that isthe same as input for ease of explanation. It should be understood thatoutput could be written in other formats (e.g., output could be directlywritten to an electronic file).

In this example, indicator 1824 indicated there is no relevant dataprogram component (i.e. one worth accelerating). Additionally, anexplicit indicator 1826 could be used to show that there was no keyprovided for partitioning and grouping data in the data component.However, this could also be inferred implicitly from checking theindicator 1824 without the use of an indicator 1826.

As shown in the flow diagram 1900, using Spark SQL 1932, the data areread from a table 1930 into a DDS of row objects (e.g., Spark rowobjects) 1934. Each Row object represents a record. The DDS 1934 isapplied to the specialized Embedded Process functionDatasetToDatasetFunction 1936, where the thread program is executed inparallel. The output produced by the thread program is stored in theoutput DDS 1946 of one or more row objects. The output DDS 1946 areinserted into the output table 1950 using a DataFrameWriter interface1948.

FIG. 19B illustrates a flow diagram 1955 for a data file 1960 accordingto program indicators corresponding to case (1). In this example, thestored data is stored at the server in an electronic file 1960.

In one or more embodiments, a computing system determines whether thethread program component specifies information for partitioning thestored data. When the thread program component does not specifyinformation for partitioning the stored data, generate thecomputer-generated computer instructions to set a value for a key forthe DDS. In this case the value can be disregarded (e.g., by setting itto a NULL value). This can indicate to the computing system todistribute the data amongst partitions based on other considerations(e.g., distributing load amongst available computing nodes). Asexplained other alternative DDS could be used (e.g., DDS <row>).

In the code generated according to flow diagram 1955, data is read fromfile 1960 into a key/value pair DDS 1964 using a File Input Format andRecord Reader 1962 (e.g. a type of File Input Format and Record Readercan be provided by Hadoop®). The record key is ignored, and the valuecontains the record data. The pair DDS 1964 is applied to thespecialized Embedded Process function FilePairToFileFunction 1982, wherethe DS2 thread program is executed in parallel. The output produced bythe thread program component is written directly to a file 1984 (e.g.,one stored in HDFS).

FIG. 20A illustrates a flow diagram 2000 for a data table 2030 accordingto program indicators corresponding to case (2). In case (2) there is athread and a data program component, none of them with a BY statement.An example DS2 thread program 2090 according to case (2) is shown inFIG. 20C. This DS2 thread program 2090 runs in two phases. The DS2thread program component 2092 runs in phase one and its tasks areexecuted in parallel. The DS2 data program component 2094 runs in phasetwo using one single task.

In one or more embodiments, a computer system determines whether theprogram comprises a relevant data program component capable of executionin parallel (e.g., data program component 2094). For instance, in thiscase parsing the code would reveal mathematical operations 2098 in thedata program component. Responsive to determining that the programcomprises the relevant data program component, the computer systemgenerates the computer-generated computer instructions to specifypartitioning and grouping the output of the thread program componentinto multiple sets of partitioned output. The computer-generatedcomputer instructions specify to distribute the multiple sets ofpartitioned output on computing nodes for performing parallel executionof the data program instructions of the data program component for eachof the multiple sets of partitioned output (e.g., as part of a secondphase).

As an example, in phase one, using Spark SQL 2032, data is read from atable 2030 into DDS 2034 (e.g., a data set of row objects where rowobject represents a record). The DDS 2034 is applied to the specializedEmbedded Process function DatasetToPairFunction 2036 where a receivedthread program component (e.g., thread program component 2092) isexecuted in parallel. The data produced by the thread program iscoalesced into a key/value pair DDS 2038.

In phase two, the coalesced pair DDS 2038 is applied to the specializedEmbedded Process function FilePairToDatasetFunction 2044, where the dataprogram component (e.g., data program component 2094) is executed as asingle task. The output produced by the data program component is storedin the output DDS 2046 (e.g., a DDS of row objects). The output DDS 2046(i.e. rows) are inserted into the output table 2050 using theDataFrameWriter interface 2048.

As mentioned with respect to other examples any DDS implemented in apair or row style in examples, could be implemented in an alternativeDDS structure with appropriate corrections to generated functions. Forexample, DDS 2038 is shown as a DDS with <K, V>, but it couldalternatively be implemented with rows where all the rows with the samekey are stored on the same DDS. In this case the Embedded Processfunction on either end would be used to do a dataset-to-dataset typefunction.

In one or more embodiments, a computing system determines whether thedata program component specifies the information for partitioning andgrouping the output of the thread program component using a key. In oneor more embodiments, when the data program component does not specifyinformation for partitioning and grouping the output of the threadprogram component using a key (e.g., in FIG. 20A), a single DDS iscreated for executing the data program on the single DDS (e.g., DDS2046).

FIG. 20B illustrates a flow diagram 2055 for a file 2060 according toprogram indicators corresponding to case (2).

In phase one, data are read from file 2060 into a key/value pair DDS2064 using a File Input Format and Record Reader (e.g., a type of FileInput Format and Record Reader is provided by Hadoop®). The record keyis ignored, and the value contains the record data. The pair DDS 2064 isapplied to the specialized Embedded Process functionFilePairToPairFunction 2074, where the thread program component (e.g.,thread program component 2092) is executed in parallel. The outputproduced by the thread program component is coalesced into a key/valuepair DDS 2080.

In phase two, the coalesced pair DDS 2080 is applied to the specializedEmbedded Process function, FilePairToFileFunction 2082 where the dataprogram component is executed as a single task. The output produced bythe data program component is written directly to an electronic file2084 (e.g., one stored in HDFS).

FIG. 21A illustrates a flow diagram 2100 for a data table 2130 accordingto program indicators according to case (3). In case (3), there is athread program component with no BY statement and a data programcomponent with a BY statement. An example DS2 thread program 2190according to case (3) is shown in FIG. 21C with a thread programcomponent 2192 and a data program component 2194. The data programcomponent 2914 contains a BY statement 2196 and computer instructions2188 comprising logical operators. The entire DS2 thread program 2190runs in two phases. The thread program component 2192 runs in phase oneand its tasks are executed in parallel. Output data from the threadprogram component can be sorted by the columns specified in the BYstatement 2196 of the data program component 2194. The DS2 data program2190 runs in phase two using one single task.

In phase one, using Spark SQL 2132, the data is read from a table 2130into a DDS 2134 (e.g., of row objects, where each row object representsa record). The DDS 2134 is applied to the specialized Embedded Processfunction DatasetToPairFunction 2136, where the thread program componentis executed in parallel. The output produced by the thread programcomponent is stored in a key/value pair DDS 2138. The DDS 2138 key isthe columns specified in the BY statement (e.g., BY statement 2196) ofthe data program component (e.g., data program component 2194). The pairDDS 2138 is sorted (e.g., SortBy Key function 2140) and coalesced intoanother pair DDS 2142 that is used as input for the next phase.

In phase two, the coalesced pair DDS 2142 is applied to the specializedEmbedded Process function FilePairToDatasetFunction 2144, where the dataprogram component is executed as a single task. The output produced bythe data program component is stored in the output DDS 2146 (e.g., ofrow objects). Output rows are inserted into the output table 2150 usingthe DataFrameWriter interface 2148.

In one or more embodiments, a computing device determines whether thedata program component specifies information for partitioning andgrouping the output of the thread program component (e.g., by parsingthe data program component and discovering BY statement 2196).Responsive to determining that the data program component specifiesinformation for partitioning and grouping the output of the threadprogram component using a particular key, a computing system describedherein generates the computer-generated computer instructions to specifysorting the output of the thread program component according to theinformation for partitioning the output of the thread program component.For instance, output data from the thread program component can besorted by car make as specified in the BY statement 2196 of the dataprogram component 2194. Additionally, or alternatively, when the dataprogram component specifies the information for partitioning andgrouping the output of the thread program component using a key, the keyof the DDS (e.g., pair DDS 2138) is set by the computing system usingthe information for partitioning output of the thread program component,and data of the DDS is sorted into another DDS (e.g., DDS 2142) forexecuting the data program.

FIG. 21B illustrates a flow diagram for a data file according to programindicators according to case (3).

As shown in phase one, data is read from a file 2160 into a key/valuepair DDS 2164 using a File Input Format and Record Reader 2162. The keyis ignored, and the value contains the record data. The pair DDS 2164 isapplied to the specialized Embedded Process functionFilePairToPairFunction 2174, where the thread program component (e.g.,thread program component 2192) is executed in parallel. The outputproduced by the thread program component is stored in a key/value pairDDS 2176. The key is the columns specified in the BY statement of thedata program component (e.g., by statement 2196 of data programcomponent 2194). The pair DDS 2176 is sorted (e.g., by SortBy Key 2178)and coalesced into another pair DDS 2180 that is used as input for thenext phase.

In phase two, the coalesced pair DDS 2180 is applied to the specializedEmbedded Process function FilePairToFileFunction 2182, where the dataprogram component (e.g., data program component 2194) is executed as asingle task. The output produced by the data program component iswritten directly to a file 2184 (e.g., stored in HDFS).

FIG. 22A illustrates a flow diagram for a data table 2230 according toprogram indicators corresponding to case (4). In case (4) there is athread program with a BY statement and no data program. An example DS2thread program 2290 according to case (4) is shown in FIG. 22C. Thethread component 2292 comprises a BY statement 2296. The input data arepartitioned and sorted within the partition by the columns specified inthe BY statement 2296 of the thread program component 2292. All recordswith a same key end up in the same partition. The thread programcomponent runs in parallel using multiple tasks. However, the dataprogram component 2294 in DS2 thread program 2290 does not contain logicworth accelerating. For instance, it contains merely SET and OUTPUToperators in the data program component 2294. Therefore, the dataprogram component 2294 is not executed in accelerated mode inside aSpark® server.

The columns specified in the BY statement of the DS2 thread program areused in the Spark SQL 2232 that uses a SELECT statement with theDISTRIBUTE BY and SORT BY clauses. Data is read from a table 2230 into apartitioned, and sorted within partition, DDS 2234 (e.g., a DDS of rowobjects). Accordingly, the sorting within partition specifies amulti-partitioning scheme 2200. The DDS 2234 is applied to thespecialized Embedded Process function DatasetToDatasetFunction 2236,where the thread program component (e.g., thread program component 2292)is executed in parallel. The output produced by the thread programcomponent is stored in the output DDS 2246 (e.g., of row objects). Theoutput rows are inserted into the output table 2250 using theDataFrameWriter interface 2248.

In one or more embodiments, a computing system determines whether athread program component (e.g., thread program component 2292) specifiesinformation for partitioning and grouping the stored data using a key.For instance, when the thread program component specifies informationfor partitioning and grouping the stored data using a key, the computingsystem can generate the computer-generated computer instructions toextract the stored data from the electronic table into multiple objectrecords (e.g., into DDS 2234) based on the key. Additionally, oralternatively, responsive to determining that the thread programcomponent specifies the information for partitioning and grouping thestored data using a key, the computing system can generate thecomputer-generated computer instructions to specify a multi-partitioningscheme 2200 (e.g., performed by a Spark server).

FIG. 22B illustrates a flow diagram for a data file according to programindicators corresponding to case (4). Data is read from a file 2260 intoa key/value pair DDS 2264 using the File Input Format and Record Reader2262.

In one or more embodiments, a computing system determines whether thethread program component specifies information for partitioning thestored data. When the thread program component specifies information forpartitioning the stored data, the computing system generates thecomputer-generated computer instructions to extract data from theelectronic file into a DDS comprising a key and value. The key is setusing a key indicated by the information.

As shown in FIG. 22B, using the columns specified in the thread programBY statement (e.g., BY statement 2296), the pair DDS 2264 is applied tothe specialized function MapToPairFunction to create a mapped pair DDS2268 (e.g. with proper key and value pair) or a first partition. Usingthe key, the mapped pair DDS 2268 is repartitioned with sorting withinpartitions 2270, specifying a second different partition of amulti-partitioning scheme. This results in a new pair DDS 2272partitioned by the key, where the keys within the partitions are sorted.The stored data is distributed onto computing nodes for performing theexecution in parallel of the one or more of the computer operations ofthe thread program component on each of the multiple sets of partitioneddata. For instance, the partitioned pair DDS 2272 is applied to thespecialized Embedded Process function FilePairToFileFunction 2282, wherethe thread program component runs in parallel. The output produced bythe thread program component is written directly to a file 2284 (e.g., afile stored in HDFS). This is because the data program component doesnot have any logic worth accelerating.

FIG. 23A illustrates a flow diagram for a data table according toprogram indicators corresponding to case (5). In case (5) there is athread program component with a BY statement and a data programcomponent with no BY statement. An example DS2 thread program 2390according to case (5) is shown in FIG. 23C. The thread program component2392 comprises a BY statement 2396. This DS2 thread program 2390 runs intwo phases. The DS2 thread program component 2392 runs in phase one andits tasks are executed in parallel. The DS2 data program component 2394runs in phase two using one single task.

In phase one, the columns specified in the BY statement of the threadprogram component (e.g., BY statement 2396 of thread program component2392) are used in the Spark® SQL 2332 that uses a SELECT statement withthe DISTRIBUTE BY and SORT BY clauses. Data is read from a table 2330into a partitioned, and sorted within partition, DDS 2334 (e.g., a DDSof row objects). Accordingly, the sorting within partition specifies amulti-partitioning scheme. The DDS 2334 is applied to the specializedEmbedded Process function DatasetToDatasetFunction 2336, where thethread program component (e.g., thread program component 2392) isexecuted in parallel.

In phase two, the pair DDS 2338 produced by the thread program componentis applied to the specialized Embedded Process functionFilePairToDatasetFunction 2344, where the data program component (e.g.,data program component 2394) runs a single task. The rows in the outputDDS 2246 produced by the data program component are inserted into theoutput table 2350 using the DataFrameWriter Insert into Table interface2348.

FIG. 23B illustrates a flow diagram for a data file according to programindicators corresponding to case (5).

In phase one, data is read from a file 2360 using the File Input Formatand Record Reader 2362 into a key/value pair DDS 2364. Using the columnsspecified in the BY statement of the thread program component (e.g.,thread program component 2392), the pair DDS 2364 is applied to thespecialized function MapToPairFunction 2366 to create a mapped pair DDS2368 with proper key and value pair of a first partition of amulti-partitioning scheme. Using the key, the mapped pair DDS2368 isrepartitioned into a second partition of the multi-partitioning schemewith Sort within Partitions 2370 resulting in a new pair DDS 2372partitioned by the key, where the keys within the partitions are sorted.The partitioned pair DDS 2372 is applied to the specialized EmbeddedProcess function FilePairToPairFunction 2374, where the thread programcomponent runs in parallel.

In phase two, the pair DDS 2376 produced by the thread program componentis applied to the specialized Embedded Process functionFilePairToFileFunction 2382, where the data program runs a single task.The output produced by the data program component (e.g., data programcomponent 2394) is written directly to a file 2384 (e.g., stored inHDFS).

FIG. 24A illustrates a flow diagram for a data table according toprogram indicators corresponding to case (6). In case (6) there is athread program component and a data program component, neither of themwith a BY statement. An example thread program 1890 according to case(6) is shown in FIG. 18. This thread program 1890 runs in two phases.The thread program component 1832 runs in phase one and its tasks areexecuted in parallel. The data program component 1836 runs in phase twousing one single task.

In this example, indicators 1820, 1822, 1824, and 1826 are all positiveindicating the presence of features 1810, 1812, 1814, and 1816 describedherein.

In one or more embodiments, a computing system generates a firstcomponent (e.g., first component 2421) of the computer-generatedcomputer instructions when a first indicator 1820 indicates the programcomprises the thread program component. Additionally, the computingsystem can generate a second component (e.g., second component 2422) ofthe computer-generated computer instructions when a second indicator1824 indicates the program comprises the data program component.Additionally, the computing system can generate a third component (e.g.,third component 2423) of the computer-generated computer instructionswhen the third indicator 1822 indicates the thread program componentspecifies the information for partitioning and grouping the stored datausing a first key. Additionally, the computing system can generate afourth component (e.g., fourth component 2424) of the computer-generatedcomputer instructions when the fourth indicator 1826 indicates whetherthe data program component specifies the information for partitioningand grouping the output of the thread program component using the secondkey. The first component, the second component, the third component andthe fourth component can all comprise different sets of computerinstructions.

In phase one, the columns specified in the BY statement of the threadprogram component (e.g., instruction 1830 of thread program component1832) are used in the Spark® SQL 2432 that uses a SELECT statement withthe DISTRIBUTE BY and SORT BY clauses. Data is read from a table 2430into a partitioned, and sorted within partition, DDS 2434 (e.g., a DDSof row objects). Accordingly, the sorting within partition specifies amulti-partitioning scheme. The DDS 2434 is applied to the specializedEmbedded Process function DatasetToPairFunction 2436, where the threadprogram component (e.g., thread program component 1832) is executed inparallel.

In phase two, the pair DDS 2438 produced by the thread program componentis sorted 2440 into a new coalesced pair DDS 2442, which is applied tothe specialized Embedded Process function FilePairToDatasetFunction2444, where the data program component (e.g., data program component1836) runs a single task. The rows in the output DDS 2446 produced bythe data program component are inserted into the output table 2450 usingthe DataFrameWriter interface 2448.

FIG. 24B illustrates a flow diagram for a data file 2460 according toprogram indicators corresponding to case (6).

In phase one, data are read from a file 2460 using the File Input Formatand Record Reader 2462 into a key/value pair DDS 2464. Using the columnsspecified in the thread program (e.g., instruction 1830), the pair DDS2464 is applied to the specialized function MapToPairFunction 2466 tocreate a mapped pair DDS 2468 with proper key and value pair.

Using the key, the mapped pair DDS 2468 is repartitioned with sortingwithin partitions 2470 resulting in a new pair DDS 2472 partitioned bythe key, where the keys within the partitions are sorted. Thepartitioned pair DDS 2472 is applied to the specialized Embedded Processfunction FilePairToPairFunction 2474, where the thread program component(e.g., thread program component 1832) runs in parallel.

In phase two, the pair DDS 2476 produced by the thread program componentis sorted 2478 and coalesced into a new pair DDS 2480, which is appliedto the specialized Embedded Process function FilePairToFileFunction2482, where the data program component (e.g., data program component1836) runs a single task. The output produced by the data programcomponent is written directly to a file 2484 (e.g., stored in HDFS).

1. A computer-program product tangibly embodied in a non-transitorymachine-readable storage medium, the computer-program product includingsystem instructions operable to cause a computing system to: receive, ata server of the computing system, a program, in a first computerlanguage, specifying computer operations on stored data, wherein thecomputing system is configured to partition the stored data intomultiple sets of partitioned data for performing parallel execution ofone or more of the computer operations on each of the multiple sets ofpartitioned data; determine whether the program comprises a threadprogram component, wherein thread operations of the thread programcomponent comprise computer instructions for execution in parallel ofthe one or more of the computer operations on each of the multiple setsof partitioned data; responsive to determining that the programcomprises a thread program component, generate, at the server,computer-generated computer instructions in a second computer languageby selecting from multiple possible options, wherein the multiplepossible options are for generating computer instructions for executing,in the second computer language that is different than the firstcomputer language, the one or more of the computer operations inparallel, wherein the selecting from the multiple options is dependenton one or more of: whether the thread program component specifies datakey information for partitioning and grouping the stored data using afirst key indicated by the data key information; whether the programcomprises a data program component comprising data program instructionsfor operations capable of execution in parallel on output data that isoutput from execution of the thread program component; and whether thedata program component specifies output key information for partitioningand grouping the output data of the thread program component using asecond key indicated by the output key information; and execute, by theserver, the program according to the computer-generated computerinstructions in the second computer language.
 2. The computer-programproduct of claim 1, wherein the system instructions are operable tocause the computing system to: receive the program from a client remotefrom the server and the computing system, the program comprisingtext-based, user-written computer instructions written by a user of theclient; execute the program on the stored data stored in the computingsystem; and transmit a result of executing the program to the client;and wherein the first computer language is a computer language readableby the client, and the second computer language is a computer languagereadable by the computing system.
 3. The computer-program product ofclaim 1, wherein the system instructions are operable to cause thecomputing system to execute the program according to thecomputer-generated computer instructions in the second computer languageby requesting the computing system to: partition the stored data intothe multiple sets of the partitioned data; and distribute a respectiveone of the multiple sets of the partitioned data to a respective one ofdifferent computing nodes of the computing system for executing the oneor more of the computer operations on the respective one of the multiplesets of the partitioned data.
 4. The computer-program product of claim1, wherein the system instructions are operable to cause the computingsystem to: set a first indicator indicating whether the programcomprises the thread program component; set a second indicatorindicating whether the program comprises the data program component; seta third indicator indicating whether the thread program componentspecifies the information for partitioning and grouping the stored datausing the first key; set a fourth indicator indicating whether the dataprogram component specifies the information for partitioning andgrouping the output data of the thread program component using thesecond key; and generate, based on checking the first indicator, thesecond indicator, the third indicator, and the fourth indicator thecomputer-generated computer instructions in the second computer languageby: generating a first component of the computer-generated computerinstructions in the second computer language when the first indicatorindicates the program comprises the thread program component; generatinga second component of the computer-generated computer instructions inthe second computer language when the second indicator indicates theprogram comprises the data program component; generating a thirdcomponent of the computer-generated computer instructions in the secondcomputer language when the third indicator indicates the thread programcomponent specifies the information for partitioning and grouping thestored data using the first key; and generating a fourth component ofthe computer-generated computer instructions in the second computerlanguage when the fourth indicator indicates whether the data programcomponent specifies the information for partitioning and grouping theoutput of the thread program component using the second key, wherein thefirst component, the second component, the third component and thefourth component comprise different sets of computer instructions. 5.The computer-program product of claim 1, wherein the system instructionsare operable to cause the computing system to: determine whether thethread program component specifies the data key information forpartitioning and grouping the stored data using the first key;responsive to determining that the thread program component specifiesthe information for partitioning and grouping the stored data using thefirst key, generate the computer-generated computer instructions in thesecond computer language to specify a multi-partitioning schemecomprising: a first partition in which the stored data is partitionedand grouped according to the first key; and a second partition,different from the first partition, in which the stored data isdistributed onto computing nodes for performing the execution inparallel of the one or more of the computer operations of the threadprogram component on each of the multiple sets of partitioned data. 6.The computer-program product of claim 1, wherein the system instructionsare operable to cause the computing system to: determine whether theprogram comprises the data program component; and responsive todetermining that the program comprises the data program component,generate the computer-generated computer instructions in the secondcomputer language to specify: partitioning and grouping the output dataof the thread program component into multiple sets of partitionedoutput; and distributing the multiple sets of partitioned output oncomputing nodes for performing parallel execution of the data programinstructions of the data program component for each of the multiple setsof partitioned output.
 7. The computer-program product of claim 6,wherein the system instructions are operable to cause the computingsystem to: determine whether the program comprises a data programcomponent comprising the data program instructions for the operationscapable of execution in parallel on the output data of the threadprogram component by parsing the program for one or more of arithmeticoperators, conditional operators, relational operators, logicaloperators, and assignment operators.
 8. The computer-program product ofclaim 6, wherein the system instructions are operable to cause thecomputing system to: determine whether the data program componentspecifies information for partitioning and grouping the output data ofthe thread program component using the second key; and responsive todetermining that the data program component specifies information forpartitioning and grouping the output of the thread program componentusing the second key, generate the computer-generated computerinstructions in the second computer language to specify sorting theoutput of the thread program component according to the information forpartitioning the output of the thread program component.
 9. Thecomputer-program product of claim 1, wherein the stored data is storedat the server in an electronic file; and wherein the system instructionsare operable to cause the computing system to: determine whether thethread program component specifies information for partitioning thestored data; when the thread program component specifies information forpartitioning the stored data, generate the computer-generated computerinstructions in the second computer language to extract data from theelectronic file into a distributed data set (DDS) set comprising a setkey and value, the set key for indexing data of the value, wherein theset key is set using the first key; when the thread program componentdoes not specify information for partitioning the stored data, generatethe computer-generated computer instructions in the second computerlanguage to set a value for a set key for the DDS, the value indicatingto distribute the data amongst partitions based on load considerations;and execute the program according to the computer-generated computerinstructions in the second computer language by generating the DDS. 10.The computer-program product of claim 1, wherein the stored data isstored at the server in an electronic table; and wherein the systeminstructions are operable to cause the computing system to: determinewhether the thread program component specifies information forpartitioning and grouping the stored data using the first key; when thethread program component specifies information for partitioning andgrouping the stored data using the first key, generate thecomputer-generated computer instructions in the second computer languageto extract the stored data from the electronic table into multipleobject records based on the first key; when the thread program componentdoes not specify information for partitioning and grouping the storeddata using the first key, generate the computer-generated computerinstructions in the second computer language to extract the stored datafrom the electronic table into a single object record; and execute theprogram according to the computer-generated computer instructions in thesecond computer language by generating one or more object records. 11.The computer-program product of claim 1, wherein the stored data isstored at the server in a first electronic file or table; and whereinthe system instructions are operable to cause the computing system to:determine whether the program comprises a data program componentcomprising data program instructions for operations capable of executionin parallel on the output data of the thread program component; when theprogram comprises the data program component: distribute the output ofthe thread program into a distributed data set (DDS) set; and write theoutput of the data program component directly to a second electronicfile or table; when the program does not comprises the data programcomponent, write the output of the thread program component directly tothe second electronic file or table; and execute the program accordingto the computer-generated computer instructions in the second computerlanguage by generating the second electronic file or table.
 12. Thecomputer-program product of claim 11, wherein the system instructionsare operable to cause the computing system to: determine whether thedata program component specifies the output key information forpartitioning and grouping the output data of the thread programcomponent using the second key; when the data program componentspecifies the information for partitioning and grouping the output ofthe thread program component using the second key: a set key of the DDSis set using the information for partitioning output of the threadprogram component; and data of the DDS is sorted into another DDS forexecuting the data program on the another DDS; and when the data programcomponent does not specify information for partitioning and grouping theoutput of the thread program component using the second key, a singleDDS is created for executing the data program on the single DDS.
 13. Thecomputer-program product of claim 1, wherein the system instructions areoperable to cause the computing system to determine whether the threadprogram component or the data program component specifies informationfor partitioning and grouping by parsing the respective programcomponent for a BY statement.
 14. The computer-program product of claim1, wherein the server is a server configured to perform event streamprocessing, machine learning, and database query.
 15. Thecomputer-program product of claim 14, wherein the server is aSPARK-compatible server; and wherein the system instructions areoperable to cause the computing system to execute the text-basedcomputer instructions by the SPARK-compatible server interpreting orcompiling the computer-generated computer instructions in the secondcomputer language.
 16. The computer-program product of claim 1, whereinthe computer-generated computer instructions in the second computerlanguage are generated in a computer language that can be executedwithout compiling an entire program before executing any computerinstructions, and instructions are operable to cause the computingsystem to begin executing the program without pre-compiling all of thecomputer-generated computer instructions in the second computerlanguage.
 17. The computer-program product of claim 16, wherein thecomputer language is one of JAVA, PYTHON, R or SCALA.
 18. Thecomputer-program product of claim 1, wherein the system instructions areoperable to cause the computing system to: receive a request to executethe program on data stored at another server; and generate thecomputer-generated computer instructions in the second computer languagein response to the request.
 19. The computer-program product of claim 1,wherein the system instructions are operable to cause the computingsystem to: receive other computer instructions that are directlyimplemented by the server without generating additionalcomputer-generated computer instructions in the second computer languagerelated to compliance with the server; and execute the other computerinstructions.
 20. The computer-program product of claim 19, wherein theother computer instructions are responsive to a user request or actionin a server remote from the computing system.
 21. A computer-implementedmethod comprising: receiving, at a server of the computing system, aprogram, in a first computer language, specifying computer operations onstored data, wherein the computing system is configured to partition thestored data into multiple sets of partitioned data for performingparallel execution of one or more of the computer operations on each ofthe multiple sets of partitioned data; determining whether the programcomprises a thread program component, wherein thread operations of thethread program component comprise computer instructions for execution inparallel of the one or more of the computer operations on each of themultiple sets of partitioned data; responsive to determining that theprogram comprises a thread program component, generating, at the server,computer-generated computer instructions in a second computer languageby selecting from multiple possible options, wherein the multiplepossible options are for generating computer instructions for executing,in the second computer language that is different than the firstcomputer language, the one or more of the computer operations inparallel, wherein the selecting from the multiple possible options isdependent on one or more of: whether the thread program componentspecifies data key information for partitioning and grouping the storeddata using a first key indicated by the data key information; whetherthe program comprises a data program component comprising data programinstructions for operations capable of execution in parallel on outputdata that is output from execution of the thread program component; andwhether the data program component specifies output key information forpartitioning and grouping the output data of the thread programcomponent using a second key indicated by the output key information;and executing, by the server, the program according to thecomputer-generated computer instructions in the second computerlanguage.
 22. The computer-implemented method of claim 21, wherein thereceiving the program comprises receiving the program from a clientremote from the server and the computing system, the program comprisingtext-based, user-written computer instructions written by a user of theclient; wherein the executing the program comprises executing theprogram on the stored data stored in the computing system; wherein thefirst computer language is a computer language readable by the client,and the second computer language is a computer language readable by thecomputing system; and wherein the method further comprises transmittinga result of executing the program to the client.
 23. (canceled)
 24. Thecomputer-implemented method of claim 21, wherein the method furthercomprises the computing system: setting a first indicator indicatingwhether the program comprises the thread program component; setting asecond indicator indicating whether the program comprises the dataprogram component; setting a third indicator indicating whether thethread program component specifies the information for partitioning andgrouping the stored data using the first key; setting a fourth indicatorindicating whether the data program component specifies the informationfor partitioning and grouping the output data of the thread programcomponent using the second key; and wherein the generating thecomputer-generated computer instructions in the second computer languagecomprises generating, based on checking the first indicator, the secondindicator, the third indicator, and the fourth indicator thecomputer-generated computer instructions in the second computer languageby: generating a first component of the computer-generated computerinstructions in the second computer language when the first indicatorindicates the program comprises the thread program component; generatinga second component of the computer-generated computer instructions inthe second computer language when the second indicator indicates theprogram comprises the data program component; generating a thirdcomponent of the computer-generated computer instructions in the secondcomputer language when the third indicator indicates the thread programcomponent specifies the information for partitioning and grouping thestored data using the first key; and generating a fourth component ofthe computer-generated computer instructions in the second computerlanguage when the fourth indicator indicates whether the data programcomponent specifies the information for partitioning and grouping theoutput of the thread program component using the second key; and whereinthe first component, the second component, the third component and thefourth component comprise different sets of computer instructions. 25.The computer-implemented method of claim 21, wherein the generating thecomputer-generated computer instructions in the second computer languagecomprises: determining whether the thread program component specifiesthe data key information for partitioning and grouping the stored datausing the first key; responsive to determining that the thread programcomponent specifies the information for partitioning and grouping thestored data using the first key, generating the computer-generatedcomputer instructions in the second computer language to specify amulti-partitioning scheme comprising: a first partition in which thestored data is partitioned and grouped according to the first key; and asecond partition, different from the first partition, in which thestored data is distributed onto computing nodes for performing theexecution in parallel of the one or more of the computer operations ofthe thread program component on each of the multiple sets of partitioneddata.
 26. The computer-implemented method of claim 21, wherein thegenerating the computer-generated computer instructions in the secondcomputer language comprises: determining whether the program comprisesthe data program component; and responsive to determining that theprogram comprises the data program component, generating thecomputer-generated computer instructions in the second computer languageto specify: partitioning and grouping the output data of the threadprogram component into multiple sets of partitioned output; anddistributing the multiple sets of partitioned output on computing nodesfor performing parallel execution of the data program instructions ofthe data program component for each of the multiple sets of partitionedoutput.
 27. The computer-implemented method of claim 21, wherein thegenerating the computer-generated computer instructions in the secondcomputer language comprises: determining whether the data programcomponent specifies information for partitioning and grouping the outputdata of the thread program component using the second key; andresponsive to determining that the data program component specifiesinformation for partitioning and grouping the output of the threadprogram component using the second key, generating thecomputer-generated computer instructions in the second computer languageto specify sorting the output of the thread program component accordingto the information for partitioning the output of the thread programcomponent.
 28. The computer-implemented method of claim 21, wherein thestored data is stored at the server in an electronic file; wherein thegenerating the computer-generated computer instructions in the secondcomputer language comprises: determining whether the thread programcomponent specifies information for partitioning the stored data; whenthe thread program component specifies information for partitioning thestored data, generating the computer-generated computer instructions inthe second computer language to extract data from the electronic fileinto a distributed data set (DDS) set comprising a set key and value,the set key for indexing data of the value, wherein the set key is setusing the first key; and when the thread program component does notspecify information for partitioning the stored data, generating thecomputer-generated computer instructions in the second computer languageto set a value for a set key for the DDS, the value indicating todistribute the data amongst partitions based on load considerations; andwherein the executing the program comprises generating the DDS.
 29. Thecomputer-implemented method of claim 21, wherein the stored data isstored at the server in an electronic table; wherein the generating thecomputer-generated computer instructions in the second computer languagecomprises: determining whether the thread program component specifiesinformation for partitioning and grouping the stored data using thefirst key; when the thread program component specifies information forpartitioning and grouping the stored data using the first key,generating the computer-generated computer instructions in the secondcomputer language to extract the stored data from the electronic tableinto multiple object records based on the first key; and when the threadprogram component does not specify information for partitioning andgrouping the stored data using the first key, generating thecomputer-generated computer instructions in the second computer languageto extract the stored data from the electronic table into a singleobject record; and wherein the executing the program comprisesgenerating one or more object records.
 30. A computing system comprisingprocessor and memory, the memory containing instructions executable bythe processor wherein the computing system is configured to: receive, ata server of the computing system, a program, in a first computerlanguage, specifying computer operations on stored data, wherein thecomputing system is configured to partition the stored data intomultiple sets of partitioned data for performing parallel execution ofone or more of the computer operations on each of the multiple sets ofpartitioned data; determine whether the program comprises a threadprogram component, wherein thread operations of the thread programcomponent comprise computer instructions for execution in parallel ofthe one or more of the computer operations on each of the multiple setsof partitioned data; responsive to determining that the programcomprises a thread program component, generate, at the server,computer-generated computer instructions in a second computer languageby selecting from multiple possible options, wherein the multiplepossible options are for generating computer instructions for executing,in the second computer language that is different than the firstcomputer language, the one or more of the computer operations inparallel, wherein the selecting from the multiple options is dependenton one or more of: whether the thread program component specifies datakey information for partitioning and grouping the stored data using afirst key indicated by the data key information; whether the programcomprises a data program component comprising data program instructionsfor operations capable of execution in parallel on output data that isoutput from execution of the thread program component; and whether thedata program component specifies output key information for partitioningand grouping the output data of the thread program component using asecond key indicated by the output key information; and execute, by theserver, the program according to the computer-generated computerinstructions in the second computer language.
 31. The computer programproduct of claim 1, wherein the system instructions are operable tocause the computing system to generate the computer-generated computerinstructions in the second computer language by: generating, responsiveto determining that the program comprises a thread program component, afirst component of the computer-generated computer instructions in thesecond computer language; determining that the thread program componentspecifies data key information for partitioning and grouping the storeddata using a first key indicated by the data key information; andgenerating, responsive to determining that the thread program componentspecifies data key information for partitioning and grouping the storeddata using a first key indicated by the data key information, a secondcomponent of the computer-generated computer instructions in the secondcomputer language, wherein the first component and the second componentcomprise different sets of computer instructions.